Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

Guo, Qianwen; Chow, Joseph Y.J.; Schonfeld, Paul

doi:10.1016/j.trc.2017.08.008

Cited by 42 publications

(20 citation statements)

References 37 publications

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“…Learning is made through the use of a normative behavioral mechanism so that online monitoring and strategic planning scenario evaluation are possible. The research also has implications for automated systems and artificially intelligent networks in the context of autonomous fleets (Guo et al, 2017) and smart cities. By structuring the learning in the same environment as the design and operation, it makes it easier in future research to design integrated learning and optimization strategies in networks.…”

Section: Resultsmentioning

confidence: 99%

Network Learning via Multiagent Inverse Transportation Problems

Nourinejad

Lai

et al. 2018

Transportation Science

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Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g. requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route behavior to infer shared network state parameters (e.g. link capacity dual prices). The inferred values are consistent with observations of each agent's optimization behavior. We prove that the method can obtain unique dual prices for a network shared by these agents in polynomial time. Four experiments are conducted. The first one, conducted on a 4-node network, verifies the methodology to obtain heterogeneous link cost parameters even when multinomial or mixed logit models would not be meaningfully estimated. The second is a parameter recovery test on the Nguyen-Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The third test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method. The last test demonstrates this learning on real data obtained from a freeway network in Queens, New York, using only real-time Google Maps queries.

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Section: Resultsmentioning

confidence: 99%

Network Learning via Multiagent Inverse Transportation Problems

Nourinejad

Lai

et al. 2018

Transportation Science

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“…Vehicle routing model Saberi and Verbas [11]; Yan et al [12]; Yao et al [13]; Ma et al [2,3]; Dessouky et al [14]; Pan et al [15]; Núñez et al [16]; Ghannadpour et al [17]; Schilde et al [18]; Kirchler and Calvo [19]; Sun et al [20]; Yu et al [13]; Tang et al [21] Coordinated application of DRT and FRT Aldaihani et al [22]; Sheu [23]; Guo et al [10]; Rahimia et al [24]; Chen and Nie [25]; Qiu et al [26] Service performance Alshalalfah and Shalaby [27]; Nourbakhsh and Ouyang [28]; Alshalalfah and Shalaby [29]; Chandra and Quadrifoglio [30,31]; Edwards and Watkins [32]; Kelly et al [33]; Engelen et al [34]; Freia et al [35]; Gschwender et al [36]; Rahimia et al [24];…”

Section: Research Content Papersmentioning

confidence: 99%

“…Responsive feeder transit (RFT) is a special form of demand-responsive transit (DRT), also known as demandresponsive connector (DRC), which is a flexible transit service because it operates in a demand responsive fashion within a service area and moves customers to/from a transfer point that connects to an RFT network [8]. RFT services have a significant advantage that they can increase the service coverage and accessibility compared with traditional fixed route transit, by offering a capacity with better alignment [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Low-freedom RFT (LFRFT), whose terminal stops and some sections of the route and middle stops are fixed when others may be limitedly adjusted, can only respond to passengers' demands to a lower degree. LFRFT is formed by partially adjusting FRT (Nourbakhsh and Ouyang [28], Yu et al [13], Guo et al [10], Lu et al [40,41], Gschwender et al [36], Alshalalfah and Shalaby [29], and so on) (shown in Table 2).…”

Section: Introductionmentioning

confidence: 99%

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Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles

Wang

Hao

et al. 2020

Journal of Advanced Transportation

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The last mile travelling problem is the most challenging part when using public transit. This study designs a high-freedom responsive feeder transit (HFRFT) system to serve at the transfer station, given vehicle routes, departure time, and service area based on demand. The proposed feeder transit system employs a travelling mode with multitype vehicles. In order to improve the operation of the HFRFT system, the optimization design methods are suggested for vehicle routes, scheduling, and service area. A mixed integer programming model and its hybrid of a metaheuristic algorithm are proposed to efficiently and integrally solve the vehicle routes and scheduling parameters according to the reservation requirements. A heuristic method is proposed to optimize the service area based on the equilibrium of system supply and demand. Case studies show that the mixed running mode of multiple models can significantly improve the seat utilization, which can also significantly reduce the number of departures and the average travel distance per passenger. The proposed service area optimization method is proved to be feasible to improve the last mile travel.

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“…This is becoming increasingly popular, as shown in Table 1 (e.g. Quadrifoglio and Li, 2009; Wang and Odoni, 2016; Djavadian and Chow, 2017;Guo et al, 2017;Shen et al, 2018). These initiatives suggest such partnerships can provide better connectivity and improve the efficiency and flexibility of the coexisting fixed-route transit service.…”

Section: Introductionmentioning

confidence: 99%

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

Rasulkhani

Chow

et al. 2019

Transportation Research Part E: Logistics and Transportation Re

Self Cite

125

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We propose a ridesharing strategy with integrated transit in which a private on-demand mobility service operator may drop off a passenger directly door-to-door, commit to dropping them at a transit station or picking up from a transit station, or to both pickup and drop off at two different stations with different vehicles. We study the effectiveness of online solution algorithms for this proposed strategy. Queueingtheoretic vehicle dispatch and idle vehicle relocation algorithms are customized for the problem. Several experiments are conducted first with a synthetic instance to design and test the effectiveness of this integrated solution method, the influence of different model parameters, and measure the benefit of such cooperation. Results suggest that rideshare vehicle travel time can drop by 40-60% consistently while passenger journey times can be reduced by 50-60% when demand is high. A case study of Long Island commuters to New York City (NYC) suggests having the proposed operating strategy can substantially cut user journey times and operating costs by up to 54% and 60% each for a range of 10-30 taxis initiated per zone. This result shows that there are settings where such service is highly warranted.

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Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

Cited by 42 publications

References 37 publications

Network Learning via Multiagent Inverse Transportation Problems

Network Learning via Multiagent Inverse Transportation Problems

Designing High-Freedom Responsive Feeder Transit System with Multitype Vehicles

A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers

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