An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

Abstract

This research proposed a feeder bus dispatching tool that reduces rides' effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses' routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six highpriority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.

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

“…Obviously, demand point i can be assigned to route k according to h ij and x k jm . Hence, a two-stage GA-based heuristic approach [7,35,36] was developed to solve this problem.…”

“…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.…”

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

“…The assumption is unable to reflect the characteristics of real traffic network, such as a one-way street or left-turn only intersection. When the shape and geometry distribution of the roads and customers are considered for model development, an integrated operation of transit (locating from unvisited locations to pick-up places) and transit routing (from pick-up places to the transportation hub), which not only reduces the travel time of residents but also improves the operation efficiency of vehicles, has been widely regarded as an effective tool in designing DRT network to the reliability of the result [4][5][6]34].…”

“…Constraint (3) indicates that an unvisited demand points must only be assigned to a visited demand point, and a visited demand point must not be assigned to another one. Constraint (4) ensures that the walking distance of passengers at an unvisited demand point does not exceed a certain threshold. Constraint (5) ensures that each car has visited at least one demand point.…”