Abstract
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.
Highlights
The demand-responsive transit (DRT) has been regarded as an essential alternative urban, suburban, and rural intelligent public transportation mode [1], which is promising to supplement the public transit networks and improve the transportation service quality
The method is established through three steps (FIGURE 2): (1) adopting a discrete choice model to account for the passenger trip choice probabilities influenced by monetary incentives, (2) aggregating passenger trip choice probabilities as demand data, and (3) adopting vehicle routing model based on pickups and deliveries problem to generate the dispatching and scheduling for vehicles
The demand-responsive transit plays an important role in urban mobility as a supplementary transportation mode
Summary
The demand-responsive transit (DRT) has been regarded as an essential alternative urban, suburban, and rural intelligent public transportation mode [1], which is promising to supplement the public transit networks and improve the transportation service quality. The incentive approach is useful for mode splitting at the macroscopic level and at the microscopic level to induce passengers and redistribute their trips and paths From this perspective, we construct the user behavior model and vehicle routing model to take a more in-depth view into the trip choice changed by incentives to reduce detours and trip times. Yuop = 0 means that the incentive would not be released to the passenger and the choice model reduces to a binary logit with alternatives of Pu and Puo. yuad = 1 means that we want to induce the passenger to change to position a for alighting Under this condition, the passenger would choose to accept the incentive to change the alighting place or insist on the original destination based on probabilities. This is the bridge to connect passengers demand with the supply provided by vehicle routing solutions
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