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In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes, and to generate an archive of routes of varying quality simultaneously.
In this paper a new method for placing bus stops is presented. The method is suitable for permanently installed new bus stops and temporarily chosen collection points for call busses as well. Moreover, our implementation of the Voronoi algorithm chooses new locations for bus stops in such a way that more bus stops are set in densely populated areas and less in less populated areas. To achieve this goal, a corresponding weighting is applied to each possible placement point, based on the number of inhabitants around this point and the points of interest, such as medical centers and department stores around this point. Using the area of Roding, a small town in Bavaria, for a case study, we show that our method is especially suitable for for rural areas, where there are few multi-family houses or apartment blocks and the area is not densely populated.
In this paper, we present a new Hybrid Genetic Search (HGS) algorithm for solving the Capacitated Vehicle Routing Problem for Pickup and Delivery (CVRPPD) as it is required for public transport in rural areas. One of the biggest peculiarities here is that a large area has to be covered with as few vehicles as possible. The basic idea of this algorithm is based on a more general version of HGS, which we adopted to solve the CVRPPD in rural areas. It also implements improvements that lead to the acceleration of the algorithm and, thereby, to a faster generation of a fastest route. We tested the algorithm on real road data from Roding, a rural district in Bavaria, Germany. Moreover, we designed an API for converting data from the Openrouteservice, so that our algorithm can be applied on real world examples as well.
In recent years, mobility solutions have experienced a significant upswing. Consequently, it has increased the importance of forecasting the number of passengers and determining the associated demand for vehicles. We analyze all bus routes in a rural area in contrast to other work that predicts just a single bus route. Some differences in bus routes in rural areas compared to cities are highlighted and substantiated by a case study data using Roding, a town in the rural district of Cham in northern Bavaria, as an example. Data collected and we selected a random forest model that lets us determine the passenger demand, bus line effectiveness, or general user behavior. The prediction accuracy of the selected model is currently 87%. The collected data helps to build new mobility-as-a-service solutions, such as on-call buses or dynamic route optimizations, as we show with our simulation.
Forschung 2019
(2019)