Abstract
This paper investigates the real-time customized bus (CB) route optimization problem, which aims to maximize the service rate for clients and profits for operators. The on-road bus has a flexible route, which can be updated based on the real-time data and route optimization solutions. A two-phase framework is established. In phase 1, the vehicle-related data including existing route and schedule, client-related data involving pick-up/drop-off location, and time windows are collected once receiving a new CB request. The second phase optimizes the bus route by establishing three nonlinear programming models under the given data from phase 1. A concept of profit difference is introduced to decide the served demand. To improve computation efficiency, a real-time search algorithm is proposed that the neighboring buses are tested one by one. Finally, a numerical study based on Sioux Falls network reveals the effectiveness of the proposed methodology. The results indicate that the real-time route optimization can be achieved within the computation time of 0.17–0.38 seconds.
Highlights
In recent years, a series of traffic problems happen due to traffic congestion in an urban area, such as increasing onroad travel time of citizens during daily commuting
The disadvantage of taking the bus is the low level of service (LOS) caused by the long travel time
In phase 1, the customized bus (CB) client claims the real-time CB request that includes pick-up location, drop-off location, time window of the desired earliest pick-up time, latest pick-up time, and latest drop-off time. Once such a request is published, the CB operator will search neighboring buses and identify vehicle location, existing route and schedule, in-vehicle and waiting clients’ location, and time window constraints. e neighboring search radius is the distance between the bus current location and latest clients’ pick-up location. e vehicle- and client-related data can be set as inputs of the optimization model in phase 2. e real-time CB route optimization can be fulfilled by rolling the bus search in phase 1 and optimization in phase 2. e profit difference among all tested buses decides the selected bus
Summary
A series of traffic problems happen due to traffic congestion in an urban area, such as increasing onroad travel time of citizens during daily commuting. CB provides the ride-hailing service that clients can make travel requests by using mobility apps before departure Clients can publish their desired pick-up/drop-off location and time windows. With the use of mobility apps, clients are allowed to make a request once he or she decides to take buses It brings a huge challenge for the CB operator to collect the data, optimize bus routes, and update the schedule in a short time. In phase 1, real-time data are collected once a new CB request is proposed It mainly includes the vehicle-related data with existing route and schedule and client-related data with pick-up/drop-off location and time windows. Conclusions and future work directions are presented in the last section
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