scholarly journals Temporally Adaptive A* Algorithm on Time-Dependent Transportation Network

2012 ◽  
pp. 183-194
Author(s):  
Nianbo Zheng ◽  
Feng Lu
Keyword(s):  
Transportation Network ◽  
Time Dependent ◽  
A Algorithm

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

Time-Dependent Intermodal A* Algorithm: Methodology and Implementation on a Large-Scale Network

2018 ◽  
Vol 2672 (47) ◽  
pp. 219-230 ◽  
Author(s):  
Ömer Verbas ◽  
Joshua Auld ◽  
Hubert Ley ◽  
Randy Weimer ◽  
Shon Driscoll
Keyword(s):  
Large Scale ◽  
Vehicular Network ◽  
Upper Bounds ◽  
Time Dependent ◽  
Regional Network ◽  
A Algorithm ◽  
Transit Network ◽  
Park And Ride ◽  
Large Scale Network ◽  
Scale Network

This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a multimodal network with transit, walking, and vehicular network links, and finds paths for the three major modes (transit, walking, driving) and any feasible combination thereof (e.g., park-and-ride). Turn penalties on the vehicular network and progressive transfer penalties on the transit network are considered for improved realism. Moreover, upper bounds to prevent excessive waiting and walking are introduced, as well as an upper bound on driving for the park-and-ride (PNR) mode. The algorithm is validated on the large-scale Chicago Regional network using real-world trips against the Google Directions API and the Regional Transit Authority router.

scholarly journals A new time-dependent shortest path algorithm for multimodal transportation network

2017 ◽  
Vol 109 ◽  
pp. 692-697 ◽  
Author(s):  
Abdelfattah Idri ◽  
Mariyem Oukarfi ◽  
Azedine Boulmakoul ◽  
Karine Zeitouni ◽  
Ali Masri
Keyword(s):  
Shortest Path ◽  
Transportation Network ◽  
Time Dependent ◽  
Shortest Path Algorithm ◽  
Multimodal Transportation ◽  
New Time

scholarly journals School bus routing problem in the stochastic and time-dependent transportation network

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202618 ◽  
Author(s):  
Shichao Sun ◽  
Zhengyu Duan ◽  
Qi Xu
Keyword(s):  
Transportation Network ◽  
Time Dependent ◽  
School Bus ◽  
Routing Problem ◽  
Bus Routing

scholarly journals Floyd-A∗Algorithm Solving the Least-Time Itinerary Planning Problem in Urban Scheduled Public Transport Network

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Yu Zhang ◽  
Jiafu Tang ◽  
Shimeng Lv ◽  
Xinggang Luo
Keyword(s):  
Lower Bound ◽  
Real World ◽  
Public Transport ◽  
Ad Hoc ◽  
A Priori ◽  
Time Dependent ◽  
Initial Time ◽  
Planning Problem ◽  
A Algorithm ◽  
Shenyang City

We consider an ad hoc Floyd-A∗algorithm to determine the a priori least-time itinerary from an origin to a destination given an initial time in an urban scheduled public transport (USPT) network. The network is bimodal (i.e., USPT lines and walking) and time dependent. The modified USPT network model results in more reasonable itinerary results. An itinerary is connected through a sequence of time-label arcs. The proposed Floyd-A∗algorithm is composed of two procedures designated as Itinerary Finder and Cost Estimator. The A∗-based Itinerary Finder determines the time-dependent, least-time itinerary in real time, aided by the heuristic information precomputed by the Floyd-based Cost Estimator, where a strategy is formed to preestimate the time-dependent arc travel time as an associated static lower bound. The Floyd-A∗algorithm is proven to guarantee optimality in theory and, demonstrated through a real-world example in Shenyang City USPT network to be more efficient than previous procedures. The computational experiments also reveal the time-dependent nature of the least-time itinerary. In the premise that lines run punctually, “just boarding” and “just missing” cases are identified.

scholarly journals A simulation-based dynamic traffic assignment model with combined modes

2014 ◽  
Vol 26 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Meng Meng ◽  
Chunfu Shao ◽  
Jingjing Zeng ◽  
Chunjiao Dong
Keyword(s):  
Simulation Model ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation Network ◽  
Time Dependent ◽  
Dynamic Traffic ◽  
Mesoscopic Simulation ◽  
Assignment Model ◽  
Management Measures ◽  
Private Car

This paper presents a dynamic traffic assignment (DTA) model for urban multi-modal transportation network by con­structing a mesoscopic simulation model. Several traffic means such as private car, subway, bus and bicycle are con­sidered in the network. The mesoscopic simulator consists of a mesoscopic supply simulator based on MesoTS model and a time-dependent demand simulator. The mode choice is si­multaneously considered with the route choice based on the improved C-Logit model. The traffic assignment procedure is implemented by a time-dependent shortest path (TDSP) al­gorithm in which travellers choose their modes and routes based on a range of choice criteria. The model is particularly suited for appraising a variety of transportation management measures, especially for the application of Intelligent Trans­port Systems (ITS). Five example cases including OD demand level, bus frequency, parking fee, information supply and car ownership rate are designed to test the proposed simulation model through a medium-scale case study in Beijing Chaoy­ang District in China. Computational results illustrate excel­lent performance and the application of the model to analy­sis of urban multi-modal transportation networks.

Sign in / Sign up

Export Citation Format

Share Document