Route Planning in Transportation Networks

Bast, Hannah; Delling, Daniel; Goldberg, Andrew V.; Müller‐Hannemann, Matthias; Pajor, Thomas; Sanders, Peter; Wagner, Dorothea; Werneck, Renato F.

doi:10.1007/978-3-319-49487-6_2

Cited by 448 publications

(414 citation statements)

References 231 publications

Supporting

Mentioning

388

Contrasting

Unclassified

Order By: Relevance

“…As the routing algorithm will be used heavily during simulation, the speed of a single route search requests is quite important. The Connection Scan Algorithm is one of the fastest algorithms currently available 13 and was hence selected.…”

Section: Public Transport In Mobitoppmentioning

confidence: 99%

Integrating public transport into mobiTopp

Briem

Buck

Mallig

et al. 2020

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

Demand and supply are both relevant for travel time in public transport. While it is obvious that the supply side in form of the timetable corresponds directly to the travel time, the demand side influences the travel time only partially, but in critical moments. During peak hours, when the demand reaches the capacity of the vehicles, the interaction between demand and supply becomes important. Overcrowded vehicles, hindering passengers to catch their chosen route, lead to longer travel times. Therefore, it is important to integrate the supply side of public transport into a travel demand model.The supply side of public transport has been integrated into the agent-based travel demand model mobiTopp. A timetable has been implemented, which is used for two purposes. First, it serves as input for the Connection Scan Algorithm, which is used by the agents to find the routes with earliest arrival time at their destinations. Second, it is used for the movement of the public transport vehicles. The model also contains capacity constraints for vehicles, which, when activated, result in a noticeable increase in travel time.

show abstract

Section: Public Transport In Mobitoppmentioning

confidence: 99%

Integrating public transport into mobiTopp

Briem

Buck

Mallig

et al. 2020

Future Generation Computer Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, there is no guarantee that all nodes in the designated area are connected to each other for those methods [20], so a common shortcoming of these methods is that they may not find an optimal path in the selected search area. For example, as the A* algorithm is heuristic, it cannot consider some of the best paths that, to avoid congestion, move to an area not explored by A*.…”

Section: Control the Scale Of The Road Networkmentioning

confidence: 99%

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

et al. 2018

View full text Add to dashboard Cite

Abstract:In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

show abstract

“…This latter is based on the classical shortest path algorithm of Dijkstra, which has been generalized to compute the exact set of nondominated solutions to go from one station to another w.r.t some predefined criteria. Details about the algorithm can be found here (Bast et al 2015).…”

Section: Mutation (Vns)mentioning

confidence: 99%

An advanced GA–VNS combination for multicriteria route planning in public transit networks

Dib

Moalic

Manier

et al. 2017

Expert Systems with Applications

View full text Add to dashboard Cite

Nowadays, passengers in urban public transport systems do not only seek a shorttime travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incsorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalisation of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighbourhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA-VNS represents the best approach in terms of computational time and solutions quality for a real world routing system.

show abstract

Route Planning in Transportation Networks

Cited by 448 publications

References 231 publications

Integrating public transport into mobiTopp

Integrating public transport into mobiTopp

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

An advanced GA–VNS combination for multicriteria route planning in public transit networks

Contact Info

Product

Resources

About