The impact of pricing and service area design on the modal shift towards demand responsive transit

Kaddoura, Ihab; Leich, Gregor; Nagel, Kai

doi:10.1016/j.procs.2020.03.152

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…These models have often been used to investigate the theoretical question of how many DRTs would be required to achieve the same level of mobility as private vehicles, finding replacement ratios ranging from approximately 2 to 40, (i.e., private vehicles replaced by one shared taxi) ( 9 ). This level of detail makes ABMs the most flexible of the model groups, useful for testing a range of hypothetical situations, such as different service types ( 13 ), service areas ( 14 ), approaches to ride pooling ( 15 – 17 ), vehicle relocation and staging strategies ( 18 – 20 ), impacts of traffic assignment ( 21 ), and cost impacts of different scenarios ( 22 ). Note that many researchers have a combined approach, utilizing a network assignment model to first generate and assign demand to yield a spatial origin–destination (OD) matrix and roadway travel speed skims, and then employing an ABM to estimate vehicle movements and passenger interactions ( 23 , 24 ).…”

Section: Introductionmentioning

confidence: 99%

Demand Responsive Transit Simulation of Wayne County, Michigan

Kagho

Hensle²,

Balać

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Demand responsive transit (DRT) can provide an alternative to private cars and complement existing public transport services. However, the successful implementation of DRT services remains a challenge as both researchers and policy makers can struggle to determine what sorts of places or cities are suitable for it. Research into car-dependent cities with poor transit accessibility is sparse. This study addresses this problem, investigating the potential of DRT service in Wayne County, U.S.A., whose dominant travel mode is private car. Using an agent-based approach, DRT is simulated as a new mobility option for this region, thereby providing insights into its impact on operational, user, and system-level performance indicators. DRT scenarios are tested for different fleet sizes, vehicle occupancy, and cost policies. The results show that a DRT service in Wayne County has a certain potential, especially to increase the mobility of lower-income individuals. However, introducing the service may slightly increase the overall vehicle kilometers traveled. Specific changes in service characteristics, like service area, pricing structure, or preemptive relocation of vehicles, might be needed to fully realize the potential of pooling riders in the proposed DRT service. The authors hope that this study serves as a starting point for understanding the impacts and potential benefits of DRT in Wayne County and similar low-density and car-dependent urban areas, as well as the service parameters needed for its successful implementation.

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Section: Introductionmentioning

confidence: 99%

Demand Responsive Transit Simulation of Wayne County, Michigan

Kagho

Hensle²,

Balać

et al. 2021

Transportation Research Record: Journal of the Transportation R

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show abstract

“…To study the impact of AMOD on transportation systems, the integration of AMOD into demand models is necessary. Opensource software packages like SimMobility (Nahmias-Biran et al, 2020;Oke et al, 2020), MATSim (Hörl et al, 2019a;Kaddoura et al, 2020), Polaris (Gurumurthy et al, 2020), and mobiTopp (Wilkes et al, 2021), as well as commercial software solutions already have capabilities to model AMOD supply and demand interactions. Most of these demand models utilize a pre-day assignment of AMOD demand, be it by iterative learning or a mode choice model.…”

Section: Literature Reviewmentioning

confidence: 99%

Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

Engelhardt

Malcolm

Dandl

et al. 2022

Front. Future Transp.

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With advances in digitization and automation, autonomous mobility on demand services have the potential to disrupt the future mobility system landscape. Ridepooling services in particular can both decrease land consumption by reducing the need for parking and increase transportation efficiency by increasing the average vehicle occupancy. Nevertheless, because ridepooling services require a sufficient user base for pooling to take effect, their performance can suffer if multiple operators offer such a service and must split the demand. This study presents a simulation framework for evaluating the impact of competition and cooperation among multiple ridepooling providers. Two different kinds of interaction via a broker platform are compared with the base cases of a single monopolistic operator and two independent operators with divided demand. In the first, the broker presents trip offers from all operators to customers (similar to a mobility-as-a-service platform), who can then freely choose an operator. In the second, a regulated broker platform can manipulate operator offers with the goal of shifting the customer-operator assignment from a user equilibrium towards a system optimum. To model adoptions of the service design depending on the different interaction scenario, a game setting is introduced. Within alternating turns between operators, operators can adapt parameters of their service (fleet size and objective function) to maximize profit. Results for a case study based on Manhattan taxi data, show that operators generate the highest profit in the broker setting while operating the largest fleet. Additionally, pooling efficiency can nearly be maintained compared to a single operator. The regulated competition benefits not only operators (profit) and cities (increased pooling efficiency), but customers also experience higher service rate, though they need to accept slightly increased waiting and travel time due to increased pooling efficiency. Contrarily, when users can decide freely, the lowest pooling efficiency and operator profit is observed.

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“…The pricing strategy is a rising problem for the DRT system to improve operational performance. The pricing and service area could influence the users' choices towards multi-modes [37]. Integrating bike-sharing with DRT operation can also increase the attractiveness and divert demand from other transportation modes [24].…”

Section: B User Incentive Approachmentioning

confidence: 99%

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

Wang

Zeng

et al. 2021

IEEE Access

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The customized bus (CB) is an alternative public transportation mode that extends the flexibility and coverage of the fixed-route transit networks. It allows passengers to make reservations for trips and arranges vehicles to serve shared rides. However, the operation performance is limited to vehicle routing, the number of stops, and the length of detour times. Extra detours and stops would not be significantly avoided if deploying high-capacity vehicles. The demand control aspect is a possible way out. Releasing incentives to passengers can attract them to aggregated locations and reduce the vehicle detour times. How to determine an appropriate incentive scheme for passengers is the critical problem. This paper presents an approach to integrating the disaggregated trip choice model with the vehicle routing model to determine incentive schemes. First, the discrete choice model is established to bridge the passengers' trip choice probabilities with the influence of monetary incentives, walking time, and travel time. A vehicle routing model based on the pickups and deliveries problem is then adopted to generate the routes and schedules of vehicles to serve the influenced passengers. The result shows that the proposed approach can reduce the total running kilometers, shorten the onboard time, and increase the profits. The analysis also suggests that passengers' sensitivity towards incentives is decisive to the result.

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The impact of pricing and service area design on the modal shift towards demand responsive transit

Cited by 26 publications

References 5 publications

Demand Responsive Transit Simulation of Wayne County, Michigan

Demand Responsive Transit Simulation of Wayne County, Michigan

Competition and Cooperation of Autonomous Ridepooling Services: Game-Based Simulation of a Broker Concept

Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems

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