An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Abstract

Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared.

Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

“…People at demand points, where passengers live or work, can walk to one of the nearest stops to get on and off the bus. In such a case, an operation of integrated pedestrian guidance (from demand points to selected stops) and transit routing (from pick-up places to transportation hubs) can find an optimal relation between the walking and riding time of residents and the operation efficiency of vehicles [1,6,7,34]. (3) Such integration of stop selection, transit routing, and frequency setting was a new research problem extending from an NP-hard vehicle route problem, and a novel heuristic algorithm was needed to efficiently resolve large-scale instances [8].…”

“…In the real world, passengers at the demand point may walk to one of the nearby stops to get on and off the bus, and their choice behaviors are decided by routes with empty seats and shorter driving time. Selecting optimal stops in candidate routes as pick-up locations with an assignment of demand points is also key to designing FTS routes [6,7]. erefore, the integrated operation of transit route design and frequency setting with stop selection is now widely regarded as an effective tool to improve service efficiency and financial status.…”

A Green Demand-Responsive Airport Shuttle Service Problem with Time-Varying Speeds

“…Both DRT and DRASS are extensions of the vehicle routing problem (VRP) and the pick-up and delivery problem (PDP), which aim at assigning all customers in demand points to vehicles located at different bus depots and designing routes to transport them from their home or workplace to destinations [1,2,5,8]. e only difference between them is that DRT strengthens the connectivity between residential areas and rail stations, while DRASS transports air passengers to airports [1,7]. ey have similar objective functions (i.e., operating cost, carbon emissions, and passenger satisfaction) and constraints (i.e., time windows, vehicle capacity, and mileage).…”

“…In general, DRT and DRASS often involve customers being picked up within specific time windows, which is related to the customers' satisfaction level in the airport shuttle service [9]. A variant of vehicle routing with time windows has been studied by researchers and can be divided into hard time windows [3,4,7,9] and soft time windows [12][13][14]. In the former, time windows cannot be violated, where the vehicles must arrive at the earliest arrival time and leave the customers before the latest departure time.…”

“…Recent research on minimizing emissions in DRASS models can be divided into two main categories. e first is the set of models where vehicle speed is assumed to be constant [1,2,7], and the second set includes models where the vehicle speed is timevarying based on different road conditions [5,8]. Although the first models are not accurate, they are suitable where there is a lack of traffic data.…”