A web-based intercept survey was designed and implemented in order to capture the response of tra... more A web-based intercept survey was designed and implemented in order to capture the response of transit riders in the Chicago metropolitan area to a variety of service disruptions. Current transit riders were intercepted in the field from November 2017 through January 2018, according to a sampling plan based on local ridership information, in order to gain a representative sample for analysis. Each participant completed a questionnaire regarding the intercepted trip, along with demographic and travel experience information. The survey included a series of stated-preference responses where the current trip is randomly disrupted and alternative travel modes are proposed with service characteristics randomly altered from a baseline scenario. This was designed to understand individual trade-offs between various mode alternatives and travel plan modification strategies under a variety of scenarios. Altogether, 659 transit riders gave responses to 2626 different disruption scenarios. In general, a plurality of riders (49%) choose to continue using transit, either waiting for service restoration or using agency-provided shuttle service, although at a decreasing rate as the travel delay increases. Fewer riders, approximately 15%, choose to alter their activity patterns altogether, while 26% would alter their travel to use either a taxi or an alternative transportation network company (TNC). Having a more detailed understanding of the behavior of riders under various disruption scenarios should allow transit agencies to better prepare for service recovery and restoration after and during local disruptions.
This paper examines a new hybrid intercontinental freight transport alternative (IFTA) that combi... more This paper examines a new hybrid intercontinental freight transport alternative (IFTA) that combines a logistics island (i.e. an offshore container port), vertical takeoff and landing (VTOL) aircraft, and ocean vessel transport. The hybrid IFTA offers a 'midway' alternative for intercontinental shippers that is cheaper (and slower) than conventional air freight, but slightly faster and significantly more reliable (albeit, more expensive) than conventional ocean vessel transport calling at busy ports that may be subject to disruptive delays. To compare the proposed hybrid IFTA with the conventional air-and ocean-based IFTAs, this paper employs a utility max-imization framework. A mathematical model determines the shipper value of time (VOT) range, normalized by payload, for which the proposed hybrid IFTA would be preferable to the conventional air-and ocean-based IFTAs. Preliminary results suggest that this range of shipper VOT, normalized by payload, is between $0.6/ton-hour and $22.9/ton-hour, indicating a potential market for the proposed hybrid IFTA might exist. Additionally, sensitivity analyses reveal several interesting insights; most notably, the attractiveness of the proposed hybrid IFTA hinges on decreasing the distance VTOL aircraft transport freight between the logistics island and an onshore warehouse or transloading facility.
Transportation Research Part C: Emerging Technologies, 2020
Providing quality transit service to travelers in low-density areas, particularly travelers witho... more Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced , and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the region's existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.
Traffic simulation is a key element of agent based models as the various agents decisions impact ... more Traffic simulation is a key element of agent based models as the various agents decisions impact and are impacted by the realized travel times and delays in the traffic network. Here we present the mesoscopic traffic flow model implemented in POLARIS transportation systems simulator. The model is mesoscopic in an attempt to obtain enough for applications that requires microscopic data while still inheriting the computational efficiency of macroscopic traffic flow models. The model is macroscopic at the link-level with dynamics based on the Newells Model and microscopic at the node level. We present link-level and network-level results for one mid-sized traffic network. The results show that the model can combine accuracy, level of details and computational efficiency.
This study develops an approach for network assignment convergence using individualized agent-bas... more This study develops an approach for network assignment convergence using individualized agent-based routing and agent-specific link cost information as well as historical travel times. Each traveler is routed individually from their starting and ending network link at a specific departure time. The approach is gap-based in two ways. First, as in previous approaches, the re-assignment decision during the convergence process is based on the gap between the routed and experienced travel time from the previous iteration. Secondly, the historical time-dependent and prevailing traffic conditions are averaged into a single expected value for each agent using a weight calculated by a modified two-parameter Weibull survival function. This weight is individualized based on the relative gap of the traveler from the previous iteration, as well as the iteration number; a novel aspect of this work. The methodology is tested on a medium-scale network of Bloomington, IL. The algorithm converges after only two iterations, which is promising as computational time of a single iteration can be high for large-scale networks.
This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a m... more This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a multimodal network with transit, walking, and vehicular network links, and finds paths for the three major modes (transit, walking, driving) and any feasible combination thereof (e.g., park-and-ride). Turn penalties on the vehicular network and progressive transfer penalties on the transit network are considered for improved realism. Moreover, upper bounds to prevent excessive waiting and walking are introduced, as well as an upper bound on driving for the park-and-ride (PNR) mode. The algorithm is validated on the large-scale Chicago Regional network using real-world trips against the Google Directions API and the Regional Transit Authority router.
This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP ... more This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP and market penetration scenarios, changes in mobility in terms of VMT and average travel time, as well as changes in energy consumption are estimated. The state-of-the-art integrated activity-based modeling (ABM) and traffic simulation software POLARIS is used to model the mobility impact. Moreover, POLARIS is coupled with AUTONOMIE to analyze the resulting fuel consumption.
The dynamic multimodal network assignment problem at the daily schedule level is addressed by int... more The dynamic multimodal network assignment problem at the daily schedule level is addressed by integrating an activity-based model and a dynamic traffic assignment tool through a unified framework. The framework achieves this integration while retaining disaggregated individualized information. The problem is formulated as a fixed-point problem, and equilibrium is achieved by minimizing the gap between the expected travel time, which is used by the activity-based model to generate the travelers' individual and household activity schedules, and their experienced travel times, simulated by the dynamic traffic assignment tool. The schedule adjustment problem for individuals and households is formulated as a linear optimization problem. Two measures—inconsistent-schedule penalty and number of households with unrealistic schedules—are defined to monitor the status of the equilibrium and convergence gap of the integrated system. To ensure convergence of the applied integration, heuristic strategies for selecting individuals for schedule adjustment and path swap are tested in a subarea network of Chicago, Illinois. Selecting individuals for schedule adjustment based on their inconsistent-schedule penalty reduces both defined measures significantly and leads to the convergence of the planned schedule and the experienced (i.e., simulated) schedule. Activity-based models (ABMs) and dynamic traffic assignment (DTA) procedures are advanced models on the demand and supply sides of transportation planning, respectively. Although they are conceptually and theoretically interrelated, in practice these models have developed along essentially independent tracks. Previous studies have sought to integrate the supply side with the demand side through various techniques, but few studies have addressed the equilibrium of users' schedules within the context of ABM-DTA integration without losing disaggregated information. Simulation-based DTA tools allow modelers to incorporate disaggregate information into the estimates of travel costs that can be fed back into the ABM. This study presents a novel equilibrium state definition for the ABM-DTA integration framework and an algorithmic procedure to achieve it that takes into consideration individual schedule consistency between ABM and DTA as well as the usual equilibrium conditions in the multimodal transportation network. Furthermore, this study develops an approach for activity schedule consistency at the DTA level as part of an overall integration framework in order to improve the rate of convergence to the overall equilibrium state for the integrated model. Seeking consistency between the demand and supply sides in a traffic assignment model has long been a problem of interest for both research and practice. Early work in this area treats the demand and network performance as static; under these simplifying assumptions it is possible to obtain analytical solutions (1–4). However, deriving analytical solutions becomes challenging in advanced traffic assignment models that consider temporal dynamics and disaggregation of individuals, as in the DTA problem and therefore ABM-DTA integration (5–7). Hence, most DTA tools used in practice have adopted a simulation-based approach to capture the dynamics of flow propagation in networks (5, 6, 8–10). Notwithstanding the large body of work on modeling network dynamics on the one hand (6, 11–17) and activity-based models of travel demand on the other (18–23), much less work in the literature has addressed the integration of ABMs and DTA. Early efforts include recognition of the importance of modeling activity–trip chains at a disaggregate level in simulation-based network assignment procedures and incorporating activity rescheduling in the integrated model framework (24–26); a more recent theoretical contribution formulated the problem in an extended network representation (27). However, the main ABM-DTA challenges for actual applications include overcoming computational burdens and properly formulating and defining the equilibrium state for the integrated system. Notwithstanding advances in computing, the former remains a significant concern for planning agencies, although perhaps less so for advanced research laboratories. The latter challenge is the principal focus of this study, along with algorithmic solution procedures and implementation techniques that also affect the computational aspects on large networks while preserving the integrity of the intended solution. Traditionally, integration schemes between the ABM and DTA involve aggregating the demand output, generated by the ABM, into time-dependent origin–destination matrices and feeding them into the DTA (28). Although this method is conceptually relatively straightforward to implement, it loses the disaggregated individualized information , which is the major advantage of utilizing activity-based models in the first place. Recent studies focus on integrating ABM and DTA while minimizing information aggregation. Lu et al. describe the
This paper introduces an integrated optimization–simulation framework for the formation, scheduli... more This paper introduces an integrated optimization–simulation framework for the formation, scheduling, and path assignment of groups at large-scale, organized events. The framework consists of network analysis, optimization, and simulation components. The first component finds sets of shortest paths for each origin in the network. The optimization component determines the group size, the departure time, and the path under capacity constraints. The objective is minimization of the travel time and matching of the preferred arrival time distribution of different demand units. The simulation component simulates each person in the network according to that person’s path and departure time determined by the previous components. The network performance characteristics supplied by the simulation inform the network analysis and optimization components for the next iteration. The framework is applied to the Jamarat process during the Hajj season in the Kingdom of Saudi Arabia to provide a schedule for 1.5 million people in a given day. The convergence of travel times, demand satisfaction, and level of service is achieved after several iterations.
This paper introduces an integrated mode choice–multimodal transit assignment model and solution ... more This paper introduces an integrated mode choice–multimodal transit assignment model and solution procedure intended for large-scale urban applications. The cross-nested logit mode choice model assigns travelers to car, transit, or park-and-ride. The dynamic multimodal transit assignment–simulation model determines minimum hyperpaths and assigns and simulates transit and park-and-ride travelers iteratively until the network approaches a state of equilibrium. After a given number of iterations, the updated transit network travel times are fed into the mode choice model and the model reassigns travelers to transit, car, or park-and-ride. The outer feedback loop between the mode choice model and the transit assignment model continues until the mode probabilities for each traveler do not change between iterations. A unique contribution of the method presented in this paper is that it reaches mode choice convergence with the use of disaggregate agents (travelers) instead of aggregate modal flows at the origin–destination level. The integrated model is successfully implemented on the Chicago Transit Agency's bus and train network in Illinois. Different procedures for reaching convergence are tested; the results suggest that a gap-based formulation is more efficient than the method of successive averages.
Abstract This paper presents a gap-based solution method for the time-dependent transit assignmen... more Abstract This paper presents a gap-based solution method for the time-dependent transit assignment problem with vehicle capacity constraints. A two-level, simulation-based methodology is proposed, which finds the least cost hyperpaths at the upper level and performs the assignment of transit travelers on the hyperpaths at the lower level. The detailed simulation of travelers and vehicles at the lower level allows modelers to capture transit network complexities such as transfers/missed connections, receiving a seat/standing and boarding/being rejected to board. This ‘hard’ implementation of vehicle capacity constraints at the lower level is aggregated into ‘soft constraints’ at the upper level for the least cost hyperpath calculation. Using a gap-based assignment procedure, user equilibrium is reached on large-scale networks in a computationally efficient manner. The algorithm is tested on the large-scale Chicago Transit Authority network. The gap-based approach outperforms the commonly used method of successive averages approach in terms of rate of convergence and quality of results. Furthermore, sensitivity analyses with respect to network parameters illustrate the robustness of the proposed two-level solution procedure.
Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic d... more Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic demand either as one-way trip, or as multiple independent trips. However, individuals’ travel patterns typically follow a sequence of trips chained together. In this study we aim at developing a general simulation-based dynamic network equilibrium algorithm for assignment of activity-trip chain demand. The trip chain of each individual trip maker is defined by the departure time at origin, sequence of activity destination locations, including the location of their intermediate destinations and their final destination, and activity duration at each of the intermediate destinations. Spatial and temporal dependency of subsequent trips on each other necessitate time and memory consuming calculations and storage of node-to-node time-dependent least generalized cost path trees, which is not practical for very large metropolitan area networks. We first propose a reformulation of the trip-based demand gap function formulation for the variational inequality formulation of the Bi-criterion Dynamic User Equilibrium (BDUE) problem. Next, we propose a solution algorithm for solving the BDUE problem with daily chain of activity-trips. Implementation of the algorithm for very large networks circumvents the need to store memory-intensive node-to-node time-dependent shortest path trees by implementing a destination-based time-dependent least generalized cost path finding algorithm, while maintaining the spatial and temporal dependency of subsequent trips. Numerical results for a real-world large scale network suggest that recognizing the dependency of multiple trips of a chain, and maintaining the departure time consistency of subsequent trips provide sharper drops in gap values, hence, the convergence could be achieved faster (compared to when trips are considered independent of each other).
Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems, 2015
Transit agencies seek to allocate their limited operational budget and fleet optimally to service... more Transit agencies seek to allocate their limited operational budget and fleet optimally to service routes in order to maximize user benefits. The Transit Network Frequency Setting Problem formulation developed in this study effectively captures the coupling between the routes and their prevailing patterns, which may have different subsets of stops visited at different times of the day. The number of riders is elastic to the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. As a result, the study bridges the gap between the operator’s perspective where the decision unit is the pattern schedule, and he user’s perspective, which perceives frequencies at the route level. Two main formulations are introduced. The first one maximizes the number of riders and the total waiting time savings under budget, fleet, policy headway and bus loading constraints; the second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership and minimum waiting time savings constraints. In both formulations, pattern headways are the decision variables. Spatial and temporal heterogeneity of ridership elasticity with respect to headway is captured. The formulations are applied to a large-scale test network for the Chicago area. The results show that a win–win solution is possible where both ridership and waiting time savings are increased, while the net cost is decreased.
Finding least cost hyperpaths in multi-modal transit networks: Methodology, algorithm and large-scale application, 2015
This paper presents a least cost hyperpath algorithm which captures the complexities that arise i... more This paper presents a least cost hyperpath algorithm which captures the complexities that arise in a transit network due to the number of transfers, the standing and overcrowding penalties, the availability of walking and biking in addition to the transit modes, and mode-specific limitations such as bike parking availability. After the problem is formulated as a mathematical program, a hybrid label setting/correcting algorithm is proposed as a solution. The multi-modal, time and approach dependent algorithm does not require spatial or temporal expansion of the network, resulting in good computational performance for large-scale applications. Scenario runs performed on the large-scale Chicago Transit Authority network validate the accuracy and performance of the algorithm.
Dynamic assignment-simulation methodology for multi-modal urban transit networks, 2015
This paper presents an integrated transit assignment-simulation tool. Finding least cost hyperpat... more This paper presents an integrated transit assignment-simulation tool. Finding least cost hyperpaths in a large-scale network and assigning travelers onto these paths is a computationally challenging problem. Moreover, modeling the spatial and temporal complexities in a transit network due to the discontinuities in transit events such as missing a connection, not receiving a seat, etc. exacerbates the issue of capturing realism. These challenges are overcome by: 1. Using a least cost hyperpath algorithm that captures the multi-modal, multi-pattern, time and approach-dependent features of a transit network in order to provide realistic optimal strategies; 2. Using a gap-based assignment approach to reach fast convergence; and 3. Developing a multi-agent particle simulation platform, which is able to capture the heterogeneities and the discontinuities in travel. The platform is tested on the Chicago Transit Authority network with 4,000 nodes and 64,000 links, where 1.25 million travelers are assigned and simulated along with 21,000 transit vehicles. The assignment-simulation framework can be used as a network evaluation tool to assist decision making at the strategic and operational levels.
Integrated frequency allocation and user assignment in multi-modal transit networks: Methodology and application to large-scale urban systems, 2015
This paper presents an integrated solution method to the frequency setting problem using optimiza... more This paper presents an integrated solution method to the frequency setting problem using optimization and assignment-simulation. Using a bi-level solution framework, an optimization problem with the objective of maximizing wait time savings under the budget, fleet size, vehicle load and policy headway constraints is run at the upper-level under the assumption that the total transit demand is fixed. At the lower-level, an assignment and simulation algorithm is run, which models the demand response to the new frequency setting and transmits updated ridership and flow values to the upper-level. The procedure is repeated until the improvement the wait time savings converges. The platform is tested on the Chicago Transit Authority network for the morning peak. The wait time savings in this experiment are converging and are found to be comparable with the results of a stand-alone frequency setting algorithm, which finds optimal solutions. The results are comparable with the optimal results of a stand-alone platform introduced in another study, which is modeling the demand response locally using elasticities as opposed to network-wide modeling using assignment. The integrated platform can be used for medium-term strategic and long-term planning decisions.
Stretching resources: Sensitivity of optimal bus frequency allocation to stop-level demand elasticities, 2015
Bus transit route frequencies in practice are often set reactively, without consideration of ride... more Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of elasticities corresponding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal elasticities based on time of day, (2) spatial elasticities via grouping stops into demand, supply and land use classes and (3) spatio-temporal elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons.
Dynamics of urban network traffic flow during a large-scale evacuation, 2014
This paper explores some of the dynamics of urban network traffic flow during a large-scale evacu... more This paper explores some of the dynamics of urban network traffic flow during a large-scale evacuation in the context of the network fundamental diagram (NFD). The structure of the evacuation demand can significantly affect network performance. A radial-shaped structure results in a more stable network recovery compared with a directional evacuation structure. This study confirms the existence of unloading-reloading hysteresis when a network is subject to successive cycles of loading and unloading. If a network undergoes a complete or near-complete recovery, the reloading path in the NFD follows almost the same path as in the initial loading. Results suggest that the linear relationship between average network flow and trip completion rate does not always hold, as previously thought. The relationship becomes highly scattered and nonlinear when the network is highly congested and under disruption and the number of adaptive drivers is sufficiently large. Frequent route switching by adaptive drivers can artificially increase the average network flow but does not necessarily increase the network output (trip completion rate). Adaptive driving increases fluctuations in the NFD; however, it reduces hysteresis and gridlock while increasing network capacity.
Optimal allocation of service frequencies over transit network routes and time periods: Formulation, solution and implementation using bus route patterns, 2013
This study proposes a formulation for the transit network frequency setting problem. The formulat... more This study proposes a formulation for the transit network frequency setting problem. The formulation provides an optimal allocation of resources over space and time while recognizing the existence of multiple service patterns along each bus route. Transit agencies must allocate their limited resources optimally to maximize user benefits, operator benefits, or a combination of the two. The coupling of the routes with the service patterns provided along all or portions of the routes is effectively captured, and the user perspective and the operator perspective are merged into one formulation. The service patterns may be scheduled with different subsets of stops for a given route. Users see the resulting combined route headways at the stops. The number of riders varies with the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. Two main formulations are introduced. The first extends work of Furth and Wilson and seeks to maximize the number of riders and the total wait time savings under budget, fleet, policy headway, and bus loading constraints. The second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership, and minimum wait time savings constraints. In both formulations, pattern headways in different time-of-day intervals are the decision variables. This paper provides the mathematical formulation underlying the proposed methodology, describes the solution method and implementation, and demonstrates, by example, important properties of the frequency setting problem in this context, including some that may at first appear counterintuitive.
Implementation and evaluation of weather-responsive traffic management strategies: Insight from different networks, 2013
This study presents the development and application of methodologies to support weather-responsiv... more This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.
A web-based intercept survey was designed and implemented in order to capture the response of tra... more A web-based intercept survey was designed and implemented in order to capture the response of transit riders in the Chicago metropolitan area to a variety of service disruptions. Current transit riders were intercepted in the field from November 2017 through January 2018, according to a sampling plan based on local ridership information, in order to gain a representative sample for analysis. Each participant completed a questionnaire regarding the intercepted trip, along with demographic and travel experience information. The survey included a series of stated-preference responses where the current trip is randomly disrupted and alternative travel modes are proposed with service characteristics randomly altered from a baseline scenario. This was designed to understand individual trade-offs between various mode alternatives and travel plan modification strategies under a variety of scenarios. Altogether, 659 transit riders gave responses to 2626 different disruption scenarios. In general, a plurality of riders (49%) choose to continue using transit, either waiting for service restoration or using agency-provided shuttle service, although at a decreasing rate as the travel delay increases. Fewer riders, approximately 15%, choose to alter their activity patterns altogether, while 26% would alter their travel to use either a taxi or an alternative transportation network company (TNC). Having a more detailed understanding of the behavior of riders under various disruption scenarios should allow transit agencies to better prepare for service recovery and restoration after and during local disruptions.
This paper examines a new hybrid intercontinental freight transport alternative (IFTA) that combi... more This paper examines a new hybrid intercontinental freight transport alternative (IFTA) that combines a logistics island (i.e. an offshore container port), vertical takeoff and landing (VTOL) aircraft, and ocean vessel transport. The hybrid IFTA offers a 'midway' alternative for intercontinental shippers that is cheaper (and slower) than conventional air freight, but slightly faster and significantly more reliable (albeit, more expensive) than conventional ocean vessel transport calling at busy ports that may be subject to disruptive delays. To compare the proposed hybrid IFTA with the conventional air-and ocean-based IFTAs, this paper employs a utility max-imization framework. A mathematical model determines the shipper value of time (VOT) range, normalized by payload, for which the proposed hybrid IFTA would be preferable to the conventional air-and ocean-based IFTAs. Preliminary results suggest that this range of shipper VOT, normalized by payload, is between $0.6/ton-hour and $22.9/ton-hour, indicating a potential market for the proposed hybrid IFTA might exist. Additionally, sensitivity analyses reveal several interesting insights; most notably, the attractiveness of the proposed hybrid IFTA hinges on decreasing the distance VTOL aircraft transport freight between the logistics island and an onshore warehouse or transloading facility.
Transportation Research Part C: Emerging Technologies, 2020
Providing quality transit service to travelers in low-density areas, particularly travelers witho... more Providing quality transit service to travelers in low-density areas, particularly travelers without personal vehicles, is a constant challenge for transit agencies. The advent of fully-autonomous vehicles (AVs) and their inclusion in mobility service fleets may allow transit agencies to offer better service and/or reduce their own capital and operational costs. This study focuses on the problem of allocating resources between transit patterns and operating (or subsidizing) shared-use AV mobility services (SAMSs) in a large metropolitan area. To address this question, a joint transit network redesign and SAMS fleet size determination problem (JTNR-SFSDP) is introduced , and a bi-level mathematical programming formulation and solution approach are presented. The upper-level problem modifies a transit network frequency setting problem (TNFSP) formulation via incorporating SAMS fleet size as a decision variable and allowing the removal of bus routes. The lower-level problem consists of a dynamic combined mode choice-traveler assignment problem (DCMC-TAP) formulation. The heuristic solution procedure involves solving the upper-level problem using a nonlinear programming solver and solving the lower-level problem using an iterative agent-based assignment-simulation approach. To illustrate the effectiveness of the modeling framework, this study uses traveler demand from Chicago along with the region's existing multimodal transit network. The computational results indicate significant traveler benefits, in terms of improved average traveler wait times, associated with optimizing the joint design of multimodal transit networks and SAMS fleets compared with the initial transit network design.
Traffic simulation is a key element of agent based models as the various agents decisions impact ... more Traffic simulation is a key element of agent based models as the various agents decisions impact and are impacted by the realized travel times and delays in the traffic network. Here we present the mesoscopic traffic flow model implemented in POLARIS transportation systems simulator. The model is mesoscopic in an attempt to obtain enough for applications that requires microscopic data while still inheriting the computational efficiency of macroscopic traffic flow models. The model is macroscopic at the link-level with dynamics based on the Newells Model and microscopic at the node level. We present link-level and network-level results for one mid-sized traffic network. The results show that the model can combine accuracy, level of details and computational efficiency.
This study develops an approach for network assignment convergence using individualized agent-bas... more This study develops an approach for network assignment convergence using individualized agent-based routing and agent-specific link cost information as well as historical travel times. Each traveler is routed individually from their starting and ending network link at a specific departure time. The approach is gap-based in two ways. First, as in previous approaches, the re-assignment decision during the convergence process is based on the gap between the routed and experienced travel time from the previous iteration. Secondly, the historical time-dependent and prevailing traffic conditions are averaged into a single expected value for each agent using a weight calculated by a modified two-parameter Weibull survival function. This weight is individualized based on the relative gap of the traveler from the previous iteration, as well as the iteration number; a novel aspect of this work. The methodology is tested on a medium-scale network of Bloomington, IL. The algorithm converges after only two iterations, which is promising as computational time of a single iteration can be high for large-scale networks.
This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a m... more This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a multimodal network with transit, walking, and vehicular network links, and finds paths for the three major modes (transit, walking, driving) and any feasible combination thereof (e.g., park-and-ride). Turn penalties on the vehicular network and progressive transfer penalties on the transit network are considered for improved realism. Moreover, upper bounds to prevent excessive waiting and walking are introduced, as well as an upper bound on driving for the park-and-ride (PNR) mode. The algorithm is validated on the large-scale Chicago Regional network using real-world trips against the Google Directions API and the Regional Transit Authority router.
This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP ... more This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP and market penetration scenarios, changes in mobility in terms of VMT and average travel time, as well as changes in energy consumption are estimated. The state-of-the-art integrated activity-based modeling (ABM) and traffic simulation software POLARIS is used to model the mobility impact. Moreover, POLARIS is coupled with AUTONOMIE to analyze the resulting fuel consumption.
The dynamic multimodal network assignment problem at the daily schedule level is addressed by int... more The dynamic multimodal network assignment problem at the daily schedule level is addressed by integrating an activity-based model and a dynamic traffic assignment tool through a unified framework. The framework achieves this integration while retaining disaggregated individualized information. The problem is formulated as a fixed-point problem, and equilibrium is achieved by minimizing the gap between the expected travel time, which is used by the activity-based model to generate the travelers' individual and household activity schedules, and their experienced travel times, simulated by the dynamic traffic assignment tool. The schedule adjustment problem for individuals and households is formulated as a linear optimization problem. Two measures—inconsistent-schedule penalty and number of households with unrealistic schedules—are defined to monitor the status of the equilibrium and convergence gap of the integrated system. To ensure convergence of the applied integration, heuristic strategies for selecting individuals for schedule adjustment and path swap are tested in a subarea network of Chicago, Illinois. Selecting individuals for schedule adjustment based on their inconsistent-schedule penalty reduces both defined measures significantly and leads to the convergence of the planned schedule and the experienced (i.e., simulated) schedule. Activity-based models (ABMs) and dynamic traffic assignment (DTA) procedures are advanced models on the demand and supply sides of transportation planning, respectively. Although they are conceptually and theoretically interrelated, in practice these models have developed along essentially independent tracks. Previous studies have sought to integrate the supply side with the demand side through various techniques, but few studies have addressed the equilibrium of users' schedules within the context of ABM-DTA integration without losing disaggregated information. Simulation-based DTA tools allow modelers to incorporate disaggregate information into the estimates of travel costs that can be fed back into the ABM. This study presents a novel equilibrium state definition for the ABM-DTA integration framework and an algorithmic procedure to achieve it that takes into consideration individual schedule consistency between ABM and DTA as well as the usual equilibrium conditions in the multimodal transportation network. Furthermore, this study develops an approach for activity schedule consistency at the DTA level as part of an overall integration framework in order to improve the rate of convergence to the overall equilibrium state for the integrated model. Seeking consistency between the demand and supply sides in a traffic assignment model has long been a problem of interest for both research and practice. Early work in this area treats the demand and network performance as static; under these simplifying assumptions it is possible to obtain analytical solutions (1–4). However, deriving analytical solutions becomes challenging in advanced traffic assignment models that consider temporal dynamics and disaggregation of individuals, as in the DTA problem and therefore ABM-DTA integration (5–7). Hence, most DTA tools used in practice have adopted a simulation-based approach to capture the dynamics of flow propagation in networks (5, 6, 8–10). Notwithstanding the large body of work on modeling network dynamics on the one hand (6, 11–17) and activity-based models of travel demand on the other (18–23), much less work in the literature has addressed the integration of ABMs and DTA. Early efforts include recognition of the importance of modeling activity–trip chains at a disaggregate level in simulation-based network assignment procedures and incorporating activity rescheduling in the integrated model framework (24–26); a more recent theoretical contribution formulated the problem in an extended network representation (27). However, the main ABM-DTA challenges for actual applications include overcoming computational burdens and properly formulating and defining the equilibrium state for the integrated system. Notwithstanding advances in computing, the former remains a significant concern for planning agencies, although perhaps less so for advanced research laboratories. The latter challenge is the principal focus of this study, along with algorithmic solution procedures and implementation techniques that also affect the computational aspects on large networks while preserving the integrity of the intended solution. Traditionally, integration schemes between the ABM and DTA involve aggregating the demand output, generated by the ABM, into time-dependent origin–destination matrices and feeding them into the DTA (28). Although this method is conceptually relatively straightforward to implement, it loses the disaggregated individualized information , which is the major advantage of utilizing activity-based models in the first place. Recent studies focus on integrating ABM and DTA while minimizing information aggregation. Lu et al. describe the
This paper introduces an integrated optimization–simulation framework for the formation, scheduli... more This paper introduces an integrated optimization–simulation framework for the formation, scheduling, and path assignment of groups at large-scale, organized events. The framework consists of network analysis, optimization, and simulation components. The first component finds sets of shortest paths for each origin in the network. The optimization component determines the group size, the departure time, and the path under capacity constraints. The objective is minimization of the travel time and matching of the preferred arrival time distribution of different demand units. The simulation component simulates each person in the network according to that person’s path and departure time determined by the previous components. The network performance characteristics supplied by the simulation inform the network analysis and optimization components for the next iteration. The framework is applied to the Jamarat process during the Hajj season in the Kingdom of Saudi Arabia to provide a schedule for 1.5 million people in a given day. The convergence of travel times, demand satisfaction, and level of service is achieved after several iterations.
This paper introduces an integrated mode choice–multimodal transit assignment model and solution ... more This paper introduces an integrated mode choice–multimodal transit assignment model and solution procedure intended for large-scale urban applications. The cross-nested logit mode choice model assigns travelers to car, transit, or park-and-ride. The dynamic multimodal transit assignment–simulation model determines minimum hyperpaths and assigns and simulates transit and park-and-ride travelers iteratively until the network approaches a state of equilibrium. After a given number of iterations, the updated transit network travel times are fed into the mode choice model and the model reassigns travelers to transit, car, or park-and-ride. The outer feedback loop between the mode choice model and the transit assignment model continues until the mode probabilities for each traveler do not change between iterations. A unique contribution of the method presented in this paper is that it reaches mode choice convergence with the use of disaggregate agents (travelers) instead of aggregate modal flows at the origin–destination level. The integrated model is successfully implemented on the Chicago Transit Agency's bus and train network in Illinois. Different procedures for reaching convergence are tested; the results suggest that a gap-based formulation is more efficient than the method of successive averages.
Abstract This paper presents a gap-based solution method for the time-dependent transit assignmen... more Abstract This paper presents a gap-based solution method for the time-dependent transit assignment problem with vehicle capacity constraints. A two-level, simulation-based methodology is proposed, which finds the least cost hyperpaths at the upper level and performs the assignment of transit travelers on the hyperpaths at the lower level. The detailed simulation of travelers and vehicles at the lower level allows modelers to capture transit network complexities such as transfers/missed connections, receiving a seat/standing and boarding/being rejected to board. This ‘hard’ implementation of vehicle capacity constraints at the lower level is aggregated into ‘soft constraints’ at the upper level for the least cost hyperpath calculation. Using a gap-based assignment procedure, user equilibrium is reached on large-scale networks in a computationally efficient manner. The algorithm is tested on the large-scale Chicago Transit Authority network. The gap-based approach outperforms the commonly used method of successive averages approach in terms of rate of convergence and quality of results. Furthermore, sensitivity analyses with respect to network parameters illustrate the robustness of the proposed two-level solution procedure.
Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic d... more Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic demand either as one-way trip, or as multiple independent trips. However, individuals’ travel patterns typically follow a sequence of trips chained together. In this study we aim at developing a general simulation-based dynamic network equilibrium algorithm for assignment of activity-trip chain demand. The trip chain of each individual trip maker is defined by the departure time at origin, sequence of activity destination locations, including the location of their intermediate destinations and their final destination, and activity duration at each of the intermediate destinations. Spatial and temporal dependency of subsequent trips on each other necessitate time and memory consuming calculations and storage of node-to-node time-dependent least generalized cost path trees, which is not practical for very large metropolitan area networks. We first propose a reformulation of the trip-based demand gap function formulation for the variational inequality formulation of the Bi-criterion Dynamic User Equilibrium (BDUE) problem. Next, we propose a solution algorithm for solving the BDUE problem with daily chain of activity-trips. Implementation of the algorithm for very large networks circumvents the need to store memory-intensive node-to-node time-dependent shortest path trees by implementing a destination-based time-dependent least generalized cost path finding algorithm, while maintaining the spatial and temporal dependency of subsequent trips. Numerical results for a real-world large scale network suggest that recognizing the dependency of multiple trips of a chain, and maintaining the departure time consistency of subsequent trips provide sharper drops in gap values, hence, the convergence could be achieved faster (compared to when trips are considered independent of each other).
Exploring trade-offs in frequency allocation in a transit network using bus route patterns: Methodology and application to large-scale urban systems, 2015
Transit agencies seek to allocate their limited operational budget and fleet optimally to service... more Transit agencies seek to allocate their limited operational budget and fleet optimally to service routes in order to maximize user benefits. The Transit Network Frequency Setting Problem formulation developed in this study effectively captures the coupling between the routes and their prevailing patterns, which may have different subsets of stops visited at different times of the day. The number of riders is elastic to the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. As a result, the study bridges the gap between the operator’s perspective where the decision unit is the pattern schedule, and he user’s perspective, which perceives frequencies at the route level. Two main formulations are introduced. The first one maximizes the number of riders and the total waiting time savings under budget, fleet, policy headway and bus loading constraints; the second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership and minimum waiting time savings constraints. In both formulations, pattern headways are the decision variables. Spatial and temporal heterogeneity of ridership elasticity with respect to headway is captured. The formulations are applied to a large-scale test network for the Chicago area. The results show that a win–win solution is possible where both ridership and waiting time savings are increased, while the net cost is decreased.
Finding least cost hyperpaths in multi-modal transit networks: Methodology, algorithm and large-scale application, 2015
This paper presents a least cost hyperpath algorithm which captures the complexities that arise i... more This paper presents a least cost hyperpath algorithm which captures the complexities that arise in a transit network due to the number of transfers, the standing and overcrowding penalties, the availability of walking and biking in addition to the transit modes, and mode-specific limitations such as bike parking availability. After the problem is formulated as a mathematical program, a hybrid label setting/correcting algorithm is proposed as a solution. The multi-modal, time and approach dependent algorithm does not require spatial or temporal expansion of the network, resulting in good computational performance for large-scale applications. Scenario runs performed on the large-scale Chicago Transit Authority network validate the accuracy and performance of the algorithm.
Dynamic assignment-simulation methodology for multi-modal urban transit networks, 2015
This paper presents an integrated transit assignment-simulation tool. Finding least cost hyperpat... more This paper presents an integrated transit assignment-simulation tool. Finding least cost hyperpaths in a large-scale network and assigning travelers onto these paths is a computationally challenging problem. Moreover, modeling the spatial and temporal complexities in a transit network due to the discontinuities in transit events such as missing a connection, not receiving a seat, etc. exacerbates the issue of capturing realism. These challenges are overcome by: 1. Using a least cost hyperpath algorithm that captures the multi-modal, multi-pattern, time and approach-dependent features of a transit network in order to provide realistic optimal strategies; 2. Using a gap-based assignment approach to reach fast convergence; and 3. Developing a multi-agent particle simulation platform, which is able to capture the heterogeneities and the discontinuities in travel. The platform is tested on the Chicago Transit Authority network with 4,000 nodes and 64,000 links, where 1.25 million travelers are assigned and simulated along with 21,000 transit vehicles. The assignment-simulation framework can be used as a network evaluation tool to assist decision making at the strategic and operational levels.
Integrated frequency allocation and user assignment in multi-modal transit networks: Methodology and application to large-scale urban systems, 2015
This paper presents an integrated solution method to the frequency setting problem using optimiza... more This paper presents an integrated solution method to the frequency setting problem using optimization and assignment-simulation. Using a bi-level solution framework, an optimization problem with the objective of maximizing wait time savings under the budget, fleet size, vehicle load and policy headway constraints is run at the upper-level under the assumption that the total transit demand is fixed. At the lower-level, an assignment and simulation algorithm is run, which models the demand response to the new frequency setting and transmits updated ridership and flow values to the upper-level. The procedure is repeated until the improvement the wait time savings converges. The platform is tested on the Chicago Transit Authority network for the morning peak. The wait time savings in this experiment are converging and are found to be comparable with the results of a stand-alone frequency setting algorithm, which finds optimal solutions. The results are comparable with the optimal results of a stand-alone platform introduced in another study, which is modeling the demand response locally using elasticities as opposed to network-wide modeling using assignment. The integrated platform can be used for medium-term strategic and long-term planning decisions.
Stretching resources: Sensitivity of optimal bus frequency allocation to stop-level demand elasticities, 2015
Bus transit route frequencies in practice are often set reactively, without consideration of ride... more Bus transit route frequencies in practice are often set reactively, without consideration of ridership elasticity to the service frequency provided. Where elasticities are used in frequency allocation, a single across the board value or two respective values for peak and off-peak are used for the entire set of routes and stops throughout the day. With growing availability of ridership data, estimation of spatially and temporally disaggregated elasticities is possible. But do these make a difference in the resulting solution to the frequency allocation problem? This study is intended to examine this question by comparing the quality of solutions obtained using an optimal frequency allocation model with different sets of elasticities corresponding to varying levels of disaggregation. Three main methodologies for estimating ridership elasticity with respect to headway are compared in the context of a transit network frequency setting framework: (1) temporal elasticities based on time of day, (2) spatial elasticities via grouping stops into demand, supply and land use classes and (3) spatio-temporal elasticities using a linear regression model. Elasticities based only on temporal aggregation result in an underestimation of the potential improvements as compared to elasticities which account for some spatial characteristics, such as land use and the opportunity to transfer. It is also important to capture longer-term effects—over a year or more—because seasonal activity patterns may bias elasticity estimates over shorter time horizons.
Dynamics of urban network traffic flow during a large-scale evacuation, 2014
This paper explores some of the dynamics of urban network traffic flow during a large-scale evacu... more This paper explores some of the dynamics of urban network traffic flow during a large-scale evacuation in the context of the network fundamental diagram (NFD). The structure of the evacuation demand can significantly affect network performance. A radial-shaped structure results in a more stable network recovery compared with a directional evacuation structure. This study confirms the existence of unloading-reloading hysteresis when a network is subject to successive cycles of loading and unloading. If a network undergoes a complete or near-complete recovery, the reloading path in the NFD follows almost the same path as in the initial loading. Results suggest that the linear relationship between average network flow and trip completion rate does not always hold, as previously thought. The relationship becomes highly scattered and nonlinear when the network is highly congested and under disruption and the number of adaptive drivers is sufficiently large. Frequent route switching by adaptive drivers can artificially increase the average network flow but does not necessarily increase the network output (trip completion rate). Adaptive driving increases fluctuations in the NFD; however, it reduces hysteresis and gridlock while increasing network capacity.
Optimal allocation of service frequencies over transit network routes and time periods: Formulation, solution and implementation using bus route patterns, 2013
This study proposes a formulation for the transit network frequency setting problem. The formulat... more This study proposes a formulation for the transit network frequency setting problem. The formulation provides an optimal allocation of resources over space and time while recognizing the existence of multiple service patterns along each bus route. Transit agencies must allocate their limited resources optimally to maximize user benefits, operator benefits, or a combination of the two. The coupling of the routes with the service patterns provided along all or portions of the routes is effectively captured, and the user perspective and the operator perspective are merged into one formulation. The service patterns may be scheduled with different subsets of stops for a given route. Users see the resulting combined route headways at the stops. The number of riders varies with the prevailing number of bus trips at a given stop, which is the combination of different pattern dispatch frequencies. Two main formulations are introduced. The first extends work of Furth and Wilson and seeks to maximize the number of riders and the total wait time savings under budget, fleet, policy headway, and bus loading constraints. The second minimizes the net cost under fleet, policy headway, bus loading, minimum ridership, and minimum wait time savings constraints. In both formulations, pattern headways in different time-of-day intervals are the decision variables. This paper provides the mathematical formulation underlying the proposed methodology, describes the solution method and implementation, and demonstrates, by example, important properties of the frequency setting problem in this context, including some that may at first appear counterintuitive.
Implementation and evaluation of weather-responsive traffic management strategies: Insight from different networks, 2013
This study presents the development and application of methodologies to support weather-responsiv... more This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.
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