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1. Multimodal mobility systems: joint optimization of transit network design and pricing

   https://doi.org/10.1145/3450267.3450540  

   Luo, Qi; Samaranayake, Samitha; Banerjee, Siddhartha (May 2021, ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems)

   null (Ed.)

   The performance of multimodal mobility systems relies on the seamless integration of conventional mass transit services and the advent of Mobility-on-Demand (MoD) services. Prior work is limited to individually improving various transport networks' operations or linking a new mode to an existing system. In this work, we attempt to solve transit network design and pricing problems of multimodal mobility systems en masse. An operator (public transit agency or private transit operator) determines frequency settings of the mass transit system, flows of the MoD service, and prices for each trip to optimize the overall welfare. A primal-dual approach, inspired by the market design literature, yields a compact mixed integer linear programming (MILP) formulation. However, a key computational challenge remains in allocating an exponential number of hybrid modes accessible to travelers. We provide a tractable solution approach through a decomposition scheme and approximation algorithm that accelerates the computation and enables optimization of large-scale problem instances. Using a case study in Nashville, Tennessee, we demonstrate the value of the proposed model. We also show that our algorithm reduces the average runtime by 60% compared to advanced MILP solvers. This result seeks to establish a generic and simple-to-implement way of revamping and redesigning regional mobility systems in order to meet the increase in travel demand and integrate traditional fixed-line mass transit systems with new demand-responsive services. 

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2. Bilevel Optimization for On-Demand Multimodal Transit Systems

   https://doi.org/https://doi.org/10.1007/978-3-030-58942-4_4  

   Basciftci B., Van Hentenryck (September 2020, Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2020. Lecture Notes in Computer Science, vol 12296. Springer,)

   Hebrard E., Musliu N. (Ed.)

   This study explores the design of an On-Demand Multimodal Transit System (ODMTS) that includes segmented mode switching models that decide whether potential riders adopt the new ODMTS or stay with their personal vehicles. It is motivated by the desire of transit agencies to design their network by taking into account both existing and latent demand, as quality of service improves. The paper presents a bilevel optimization where the leader problem designs the network and each rider has a follower problem to decide her best route through the ODMTS. The bilevel model is solved by a decomposition algorithm that combines traditional Benders cuts with combinatorial cuts to ensure the consistency of mode choices by the leader and follower problems. The approach is evaluated on a case study using historical data from Ann Arbor, Michigan, and a user choice model based on the income levels of the potential transit riders. 

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3. Auction-Based Permit Allocation and Sharing System (A-PASS) for Travel Demand Management

   https://doi.org/10.1287/trsc.2021.1093  

   Li, Ruijie; Nie, Yu; Liu, Xiaobo (March 2022, Transportation Science)

   This paper proposes a novel quantity-based demand management system that aims to promote ridesharing. The system sells a time-dependent permit to access a road facility (conceptualized as a bottleneck) by auction but encourages commuters to share permits with each other. The commuters may be assigned one of three roles: solo driver, ridesharing driver, or rider. At the core of this auction-based permit allocation and sharing system (A-PASS) is a trilateral matching problem (TMP) that matches permits, drivers, and riders. Formulated as an integer program, TMP is first shown to be tightly bounded by its linear relaxation. A pricing policy based on the classical Vickrey–Clarke–Groves (VCG) mechanism is then devised to determine the payment of each commuter. We prove that, under the VCG policy, different commuters pay exactly the same price as long as their role and access time are the same. Importantly, by controlling the number of shared rides, any deficit that may arise from the VCG policy can be eliminated. This may be achieved with a relatively small loss to system efficiency, thanks to the revenue generated from selling permits. Results of a numerical experiment suggest A-PASS strongly promotes ridesharing. As sharing increases, all stakeholders are better off: the ridesharing platform receives greater profits, the commuters enjoy higher utility, and society benefits from more efficient utilization of the road infrastructure. 

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4. Bikeshare Users on a Budget? Trip Chaining Analysis of Bikeshare User Groups in Chicago

   https://doi.org/10.1177/0361198119838261  

   Yang, Siyue; Brakewood, Candace; Nicolas, Virgile; Sion, Jake (April 2019, Transportation Research Record: Journal of the Transportation Research Board)

   This analysis focuses on a smartphone app known as “Transit” that is used to unlock shared bicycles in Chicago. Data from the app were utilized in a three-part analysis. First, Transit app bikeshare usage patterns were compared with system-wide bikeshare utilization using publicly available data. The results revealed that hourly usage on weekdays generally follows classical peaked commuting patterns; however, daily usage reached its highest level on weekends. This suggests that there may be large numbers of both commuting and recreational users. The second part aimed to identify distinct user groups via cluster analysis; the results revealed six different clusters: (1) commuters, (2) utility users, (3) leisure users, (4) infrequent commuters, (5) weekday visitors, and (6) weekend visitors. The group unlocking the most shared bikes (45.58% of all Transit app unlocks) was commuters, who represent 10% of Transit app bikeshare users. The third part proposed a trip chaining algorithm to identify “trip chaining bikers.” This term refers to bikeshare users who return a shared bicycle and immediately check out another, presumably to avoid paying extra usage fees for trips over 30 min. The algorithm revealed that 27.3% of Transit app bikeshare users exhibited this type of “bike chaining” behavior. However, this varied substantially between user groups; notably, 66% of Transit app bikeshare users identified as commuters made one or more bike chaining unlocks. The implications are important for bikeshare providers to understand the impact of pricing policies, particularly in encouraging the turn-over of bicycles. 

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5. A Traveler Incentive Program for Promoting Community-Based Ridesharing

   https://doi.org/10.1287/trsc.2021.1121  

   Tafreshian, Amirmahdi; Masoud, Neda (January 2022, Transportation Science)

   Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a traveler incentive program (TIP) to promote community-based ridesharing with a ride back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides and (2) encourage a small, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time, budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program. 

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