Stefano Bortolomiol, Virginie Lurkin, M. Bierlaire
Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models, according to random utility theory. Compared with aggregate demand models, the added value of discrete choice models is the possibility to account for more complex and precise representations of individual behaviors. Because of the form of the resulting demand functions, there is no guarantee that an equilibrium solution for the given market exists, nor is it possible to rely on derivative-based methods to find one. Therefore, we propose a model-based algorithmic approach to find approximate equilibria, which is structured as follows. A heuristic reduction of the search space is initially performed. Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. The optimal solution of the subgame is compared against the best responses of all suppliers over the strategy sets of the original game. Best response strategies are added to the restricted problem until all ε-equilibrium conditions are satisfied simultaneously. Numerical experiments show that our methodology can approximate the results of an exact method that finds a pure equilibrium in the case of a multinomial logit model of demand with a single-product offer and homogeneous demand. Furthermore, it succeeds at finding approximate equilibria for two transportation case studies featuring more complex discrete choice models, heterogeneous demand, a multiproduct offer by suppliers, and price differentiation for which no analytical approach exists.
{"title":"A Simulation-Based Heuristic to Find Approximate Equilibria with Disaggregate Demand Models","authors":"Stefano Bortolomiol, Virginie Lurkin, M. Bierlaire","doi":"10.1287/trsc.2021.1071","DOIUrl":"https://doi.org/10.1287/trsc.2021.1071","url":null,"abstract":"Oligopolistic competition occurs in various transportation markets. In this paper, we introduce a framework to find approximate equilibrium solutions of oligopolistic markets in which demand is modeled at the disaggregate level using discrete choice models, according to random utility theory. Compared with aggregate demand models, the added value of discrete choice models is the possibility to account for more complex and precise representations of individual behaviors. Because of the form of the resulting demand functions, there is no guarantee that an equilibrium solution for the given market exists, nor is it possible to rely on derivative-based methods to find one. Therefore, we propose a model-based algorithmic approach to find approximate equilibria, which is structured as follows. A heuristic reduction of the search space is initially performed. Then, a subgame equilibrium problem is solved using a mixed integer optimization model inspired by the fixed-point iteration algorithm. The optimal solution of the subgame is compared against the best responses of all suppliers over the strategy sets of the original game. Best response strategies are added to the restricted problem until all ε-equilibrium conditions are satisfied simultaneously. Numerical experiments show that our methodology can approximate the results of an exact method that finds a pure equilibrium in the case of a multinomial logit model of demand with a single-product offer and homogeneous demand. Furthermore, it succeeds at finding approximate equilibria for two transportation case studies featuring more complex discrete choice models, heterogeneous demand, a multiproduct offer by suppliers, and price differentiation for which no analytical approach exists.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"37 1","pages":"1025-1045"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75306418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose an approximation method for estimating the probability [Formula: see text] of searching for on-street parking longer than time [Formula: see text] from the start of a parking search near a given destination [Formula: see text] based on high-resolution maps of parking demand and supply in a city. We verify the method by comparing its outcomes to the estimates obtained with an agent-based simulation model of on-street parking search. As a practical example, we construct maps of cruising time for the Israeli city of Bat Yam and demonstrate that, despite the low overall demand-to-supply ratio of 0.65, excessive demand in the city center results in a significant share of parking searches that last longer than 5 or even 10 minutes. We discuss the application of the proposed approach for urban planning.
{"title":"Approximation Method for Estimating Search Times for On-Street Parking","authors":"N. Fulman, I. Benenson","doi":"10.1287/trsc.2021.1067","DOIUrl":"https://doi.org/10.1287/trsc.2021.1067","url":null,"abstract":"We propose an approximation method for estimating the probability [Formula: see text] of searching for on-street parking longer than time [Formula: see text] from the start of a parking search near a given destination [Formula: see text] based on high-resolution maps of parking demand and supply in a city. We verify the method by comparing its outcomes to the estimates obtained with an agent-based simulation model of on-street parking search. As a practical example, we construct maps of cruising time for the Israeli city of Bat Yam and demonstrate that, despite the low overall demand-to-supply ratio of 0.65, excessive demand in the city center results in a significant share of parking searches that last longer than 5 or even 10 minutes. We discuss the application of the proposed approach for urban planning.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"92 1","pages":"1046-1069"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80879079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The single source Weber problem with limited distances (SSWPLD) is a continuous optimization problem in location theory. The SSWPLD algorithms proposed so far are based on the enumeration of all regions of [Formula: see text] defined by a given set of n intersecting circumferences. Early algorithms require [Formula: see text] time for the enumeration, but they were recently shown to be incorrect in case of degenerate intersections, that is, when three or more circumferences pass through the same intersection point. This problem was fixed by a modified enumeration algorithm with complexity [Formula: see text], based on the construction of neighborhoods of degenerate intersection points. In this paper, it is shown that the complexity for correctly dealing with degenerate intersections can be reduced to [Formula: see text] so that existing enumeration algorithms can be fixed without increasing their [Formula: see text] time complexity, which is due to some preliminary computations unrelated to intersection degeneracy. Furthermore, a new algorithm for enumerating all regions to solve the SSWPLD is described: its worst-case time complexity is [Formula: see text]. The new algorithm also guarantees that the regions are enumerated only once.
{"title":"A New Algorithm for the Single Source Weber Problem with Limited Distances","authors":"G. Righini","doi":"10.1287/trsc.2021.1083","DOIUrl":"https://doi.org/10.1287/trsc.2021.1083","url":null,"abstract":"The single source Weber problem with limited distances (SSWPLD) is a continuous optimization problem in location theory. The SSWPLD algorithms proposed so far are based on the enumeration of all regions of [Formula: see text] defined by a given set of n intersecting circumferences. Early algorithms require [Formula: see text] time for the enumeration, but they were recently shown to be incorrect in case of degenerate intersections, that is, when three or more circumferences pass through the same intersection point. This problem was fixed by a modified enumeration algorithm with complexity [Formula: see text], based on the construction of neighborhoods of degenerate intersection points. In this paper, it is shown that the complexity for correctly dealing with degenerate intersections can be reduced to [Formula: see text] so that existing enumeration algorithms can be fixed without increasing their [Formula: see text] time complexity, which is due to some preliminary computations unrelated to intersection degeneracy. Furthermore, a new algorithm for enumerating all regions to solve the SSWPLD is described: its worst-case time complexity is [Formula: see text]. The new algorithm also guarantees that the regions are enumerated only once.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"126 1","pages":"1136-1150"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76830806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, because of its fixed structure. For this reason, mass transit is evolving toward some degree of flexibility. Although the extension of Dial-a-Ride systems to general public meets such need of adaptability, it presents several drawbacks mostly related to the their extreme flexibility. Consequently, new transportation alternatives, such as demand-adaptive systems (DASs), combining characteristics from both the traditional transit and Dial-a-Ride, have been introduced. For their twofold nature, DASs require careful planning. We focus on tactical aspects of the planning process by formalizing the single-line DAS design problem with stationary demand and proposing two alternative hierarchical decomposition approaches for its solution. The main motivation behind this work is to provide a general methodology suitable to be used as a tool to build the tactical DAS plan in real-life conditions. We provide an experimental study where the two proposed decomposition methods are compared and the general behavior of the systems is analyzed when altering some design parameters. Furthermore, we test the versatility of our methods on a variety of situation that may be encountered in real-life conditions.
{"title":"The Single-Line Design Problem for Demand-Adaptive Transit Systems: A Modeling Framework and Decomposition Approach for the Stationary-Demand Case","authors":"F. Errico, T. Crainic, F. Malucelli, M. Nonato","doi":"10.1287/trsc.2021.1062","DOIUrl":"https://doi.org/10.1287/trsc.2021.1062","url":null,"abstract":"When demand for transportation is low or sparse, traditional transit cannot provide efficient and good-quality service, because of its fixed structure. For this reason, mass transit is evolving toward some degree of flexibility. Although the extension of Dial-a-Ride systems to general public meets such need of adaptability, it presents several drawbacks mostly related to the their extreme flexibility. Consequently, new transportation alternatives, such as demand-adaptive systems (DASs), combining characteristics from both the traditional transit and Dial-a-Ride, have been introduced. For their twofold nature, DASs require careful planning. We focus on tactical aspects of the planning process by formalizing the single-line DAS design problem with stationary demand and proposing two alternative hierarchical decomposition approaches for its solution. The main motivation behind this work is to provide a general methodology suitable to be used as a tool to build the tactical DAS plan in real-life conditions. We provide an experimental study where the two proposed decomposition methods are compared and the general behavior of the systems is analyzed when altering some design parameters. Furthermore, we test the versatility of our methods on a variety of situation that may be encountered in real-life conditions.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"23 1","pages":"1300-1321"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91275193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lennart Baardman, K. J. Roodbergen, H. J. Carlo, Albert H. Schrotenboer
This study focuses on the problem of sequencing requests for an end-of-aisle automated storage and retrieval system in which each retrieved load must be returned to its earlier storage location after a worker has picked some products from the load. At the picking station, a buffer is maintained to absorb any fluctuations in speed between the worker and the storage/retrieval machine. We show that, under conditions, the problem of optimally sequencing the requests in this system with a buffer size of m loads forms a special case of the multiple traveling salesmen problem in which each salesman visits the same number of cities. Several interesting structural properties for the problem are mathematically shown. In addition, a branch-and-cut method and heuristics are proposed. Experimental results show that the proposed simulated annealing-based heuristic performs well in all circumstances and significantly outperforms benchmark heuristics. For instances with negligible picking times for the worker, we show that this heuristic provides solutions that are, on average, within 1.8% from the optimal value.
{"title":"A Special Case of the Multiple Traveling Salesmen Problem in End-of-Aisle Picking Systems","authors":"Lennart Baardman, K. J. Roodbergen, H. J. Carlo, Albert H. Schrotenboer","doi":"10.1287/trsc.2021.1075","DOIUrl":"https://doi.org/10.1287/trsc.2021.1075","url":null,"abstract":"This study focuses on the problem of sequencing requests for an end-of-aisle automated storage and retrieval system in which each retrieved load must be returned to its earlier storage location after a worker has picked some products from the load. At the picking station, a buffer is maintained to absorb any fluctuations in speed between the worker and the storage/retrieval machine. We show that, under conditions, the problem of optimally sequencing the requests in this system with a buffer size of m loads forms a special case of the multiple traveling salesmen problem in which each salesman visits the same number of cities. Several interesting structural properties for the problem are mathematically shown. In addition, a branch-and-cut method and heuristics are proposed. Experimental results show that the proposed simulated annealing-based heuristic performs well in all circumstances and significantly outperforms benchmark heuristics. For instances with negligible picking times for the worker, we show that this heuristic provides solutions that are, on average, within 1.8% from the optimal value.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"30 1","pages":"1151-1169"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75424832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study cooperation among hinterland container transport operators that may share transport capacity and demand in corridors between inland and sea ports. We model this transportation problem as a minimum cost flow problem and assume that operators share the total cost based on a bargaining outcome, which has been proven equivalent to the Shapley value. To examine the stability of such cooperation, we perform a sensitivity analysis of the membership of the Shapley value (the bargaining outcome) to the core (the set of stable outcomes) by leveraging a novel concept of parametric cooperative games. We obtain closed-form solutions for identical players that explicitly characterize the impact of overcapacity on the stability of cooperation. For more general cases, we develop a computational approach based on parametric optimization techniques. The numerical results indicate that our primary analytical result, that is, that overcapacity undermines stability, is generally valid, and that overcapacitated networks may permit stable cooperation in only a limited range of settings.
{"title":"An Analysis of the Stability of Hinterland Container Transport Cooperation","authors":"A. Giudici, T. Lu, Clemens Thielen, R. Zuidwijk","doi":"10.1287/trsc.2021.1050","DOIUrl":"https://doi.org/10.1287/trsc.2021.1050","url":null,"abstract":"We study cooperation among hinterland container transport operators that may share transport capacity and demand in corridors between inland and sea ports. We model this transportation problem as a minimum cost flow problem and assume that operators share the total cost based on a bargaining outcome, which has been proven equivalent to the Shapley value. To examine the stability of such cooperation, we perform a sensitivity analysis of the membership of the Shapley value (the bargaining outcome) to the core (the set of stable outcomes) by leveraging a novel concept of parametric cooperative games. We obtain closed-form solutions for identical players that explicitly characterize the impact of overcapacity on the stability of cooperation. For more general cases, we develop a computational approach based on parametric optimization techniques. The numerical results indicate that our primary analytical result, that is, that overcapacity undermines stability, is generally valid, and that overcapacitated networks may permit stable cooperation in only a limited range of settings.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"39 1","pages":"1170-1186"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77545977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although urban transit systems (UTS) often have fixed vehicle capacity and relatively constant departure headways, they may need to accommodate dramatically fluctuating passenger demands over space and time, resulting in either excessive passenger waiting or vehicle capacity and energy waste. Therefore, on the one hand, optimal operations of UTS rely on accurate modeling of passenger queuing dynamics, which is particularly complex on a multistop transit corridor. On the other hand, capacities of transit vehicles can be made variable and adaptive to time-variant passenger demand so as to minimize energy waste, especially with the emergence of modular vehicle technologies. This paper investigates operations of a multistop transit corridor in which vehicles may have different capacities across dispatches. We specify skewed time coordinates to simplify the problem structure while incorporating traffic congestion. Then, we propose a mixed integer linear programming model that determines the optimal dynamic headways and vehicle capacities over the study time horizon to minimize the overall system cost for the transit corridor. In particular, the proposed model considers a realistic multistop first-in, first-out (MSFIFO) rule that gives the same boarding priority to passengers arriving at a station in the same time interval yet with different destinations. A customized dynamic programming (DP) algorithm is proposed to solve this model efficiently. To circumvent the rapid increase of the state space of a typical DP algorithm, we analyze the theoretical properties of the investigated problem and identify upper and lower bounds to a feasible solution. The bounds largely reduce the state space during the DP iterations and greatly improve the efficiency of the proposed DP algorithm. The state dimensions are also reduced with the MSFIFO rule such that all queues with different destinations at the same origin can be tracked with a single boarding position state variable at each stage. A hypothetical example and a real-world case study show that the proposed DP algorithm greatly outperforms a state-of-the-art commercial solver (Gurobi) in both solution quality and time.
{"title":"Operations Design of Modular Vehicles on an Oversaturated Corridor with First-in, First-out Passenger Queueing","authors":"Xiaowei Shi, X. Li","doi":"10.1287/trsc.2021.1074","DOIUrl":"https://doi.org/10.1287/trsc.2021.1074","url":null,"abstract":"Although urban transit systems (UTS) often have fixed vehicle capacity and relatively constant departure headways, they may need to accommodate dramatically fluctuating passenger demands over space and time, resulting in either excessive passenger waiting or vehicle capacity and energy waste. Therefore, on the one hand, optimal operations of UTS rely on accurate modeling of passenger queuing dynamics, which is particularly complex on a multistop transit corridor. On the other hand, capacities of transit vehicles can be made variable and adaptive to time-variant passenger demand so as to minimize energy waste, especially with the emergence of modular vehicle technologies. This paper investigates operations of a multistop transit corridor in which vehicles may have different capacities across dispatches. We specify skewed time coordinates to simplify the problem structure while incorporating traffic congestion. Then, we propose a mixed integer linear programming model that determines the optimal dynamic headways and vehicle capacities over the study time horizon to minimize the overall system cost for the transit corridor. In particular, the proposed model considers a realistic multistop first-in, first-out (MSFIFO) rule that gives the same boarding priority to passengers arriving at a station in the same time interval yet with different destinations. A customized dynamic programming (DP) algorithm is proposed to solve this model efficiently. To circumvent the rapid increase of the state space of a typical DP algorithm, we analyze the theoretical properties of the investigated problem and identify upper and lower bounds to a feasible solution. The bounds largely reduce the state space during the DP iterations and greatly improve the efficiency of the proposed DP algorithm. The state dimensions are also reduced with the MSFIFO rule such that all queues with different destinations at the same origin can be tracked with a single boarding position state variable at each stage. A hypothetical example and a real-world case study show that the proposed DP algorithm greatly outperforms a state-of-the-art commercial solver (Gurobi) in both solution quality and time.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"24 1","pages":"1187-1205"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88488880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, there are attempts to utilize drones in the logistic application. We consider the case in which there are multiple drones with different characteristics, such as speed and battery capacity. The truck and drone collaborate the delivery to serve all customers, while the drones are carried by and dispatched from the truck. The multiple drones can be deployed simultaneously; however, the truck must wait until all drones return. Therefore, the goal is to minimize the total sum of truck travel and waiting times for drones to return after deliveries. We call the proposed model a heterogeneous drone-truck routing problem (HDTRP), and a mixed-integer programming formulation for the problem is presented. We develop an exact algorithm based on the logic-based Benders decomposition approach, which outperforms the state-of-the-art solvers.
{"title":"An Exact Algorithm for Heterogeneous Drone-Truck Routing Problem","authors":"Munjeong Kang, Chungmok Lee","doi":"10.1287/trsc.2021.1055","DOIUrl":"https://doi.org/10.1287/trsc.2021.1055","url":null,"abstract":"Recently, there are attempts to utilize drones in the logistic application. We consider the case in which there are multiple drones with different characteristics, such as speed and battery capacity. The truck and drone collaborate the delivery to serve all customers, while the drones are carried by and dispatched from the truck. The multiple drones can be deployed simultaneously; however, the truck must wait until all drones return. Therefore, the goal is to minimize the total sum of truck travel and waiting times for drones to return after deliveries. We call the proposed model a heterogeneous drone-truck routing problem (HDTRP), and a mixed-integer programming formulation for the problem is presented. We develop an exact algorithm based on the logic-based Benders decomposition approach, which outperforms the state-of-the-art solvers.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"33 1","pages":"1088-1112"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91005459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing’s effects. Taking advantage of Uber’s staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber’s effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber’s entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings.
{"title":"The Heterogeneous Effects of P2P Ride-Hailing on Traffic: Evidence from Uber's Entry in California","authors":"Suvrat S. Dhanorkar, Gordon Burtch","doi":"10.1287/trsc.2021.1077","DOIUrl":"https://doi.org/10.1287/trsc.2021.1077","url":null,"abstract":"Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing’s effects. Taking advantage of Uber’s staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber’s effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber’s entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"22 1","pages":"750-774"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86003197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.
{"title":"Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty","authors":"Lei Xu, T. S. Ng, A. Costa","doi":"10.1287/trsc.2021.1040","DOIUrl":"https://doi.org/10.1287/trsc.2021.1040","url":null,"abstract":"In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"14 1","pages":"1206-1225"},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86204966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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