Research on the new route choice method of main optical cables based on Dijkstra algorithm: a case study of Guanyun County

2007 ◽  
Author(s):  
Mei Zhang ◽  
Xiaoyun Gu ◽  
Zhenjie Chen ◽  
Xue Li ◽  
Mo Su
Keyword(s):  
Route Choice ◽  
Dijkstra Algorithm ◽  
Choice Method

scholarly journals Road-safety recognition and network equilibrium with perceived route-choice sets

2019 ◽  
Vol 1 (2) ◽  
pp. 126-134
Author(s):  
Mao-sheng Li ◽  
He-lai Huang
Keyword(s):  
Convex Combination ◽  
Route Choice ◽  
User Equilibrium ◽  
Equilibrium Analysis ◽  
Combination Method ◽  
Crash Frequency ◽  
Traffic Demand ◽  
Additional Costs ◽  
Choice Set

Abstract Safety is regarded as the second basic need in Maslow’s hierarchy of needs (1943), and safety recognition and circumvention behaviour in the route-choice decision-making process should therefore be accommodated in network-traffic equilibrium analysis frameworks. This paper proposes a framework by which crash frequency, forecasted using the safety-analysis method or compiled from historical data for intersections, is used to measure the safety consciousness of drivers. Drivers are then classified into different groups according to their acceptable-risk thresholds, and each group has its own route-choice set. Decision behaviour whereby drivers are willing to bear additional costs in order to circumvent travel risk is incorporated into the variational inequality model based on the user equilibrium in the perceived route-choice set (UE-PRCS), which is an extension of Wardrop’s first principle. The Frank–Wolfe algorithm, based on the convex combination method, is employed to obtain the solution. A small road network is used as a case study to illustrate the proposed framework, incorporating risk recognition and circumvention behaviour under different combinations of traffic demand and risk-sensitivity group ratio. The results show that the standard user equilibrium is a special case of the UE-PRCS, but that the UE traffic state is more common than the UE-PRCS under different parameters.

Study of the Logistics Transportation Vehicle Terminal Path Optimization and Algorithm Based on GIS

2014 ◽  
Vol 644-650 ◽  
pp. 2249-2252 ◽  
Author(s):  
Yan Liu ◽  
Ying Ju Li ◽  
Yan Xiao Zhang
Keyword(s):  
Choice Model ◽  
Route Choice ◽  
Dijkstra Algorithm ◽  
Distribution Centers ◽  
Distribution Problem ◽  
Logistics Distribution ◽  
Operation Speed ◽  
Set Up ◽  
Transportation Vehicle ◽  
Vehicle Terminal

Based on feasibility and rationality, the path of the distribution centers, cargo terminal and receiving point as the optimized object.Using the Dijkstra algorithm and to improve it,set up customer logistics cost minimum as the target of route choice model;Using the improved Dijkstra algorithm greatly improves the operation speed,especially in the cross area for the shortest path,division graph method has very big enhancement to the time complexity of the algorithm ,enable the customer logistics total cost minimum.Therefore, route choice model can accurately describe the logistics distribution problem.

scholarly journals Modelling cyclists’ route choice using Strava and OSMnx: A case study of the City of Glasgow

2021 ◽  
Vol 9 ◽  
pp. 100301
Author(s):  
Mohammad Anwar Alattar ◽  
Caitlin Cottrill ◽  
Mark Beecroft
Keyword(s):  
Route Choice ◽  
The City

scholarly journals Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Li ◽  
Qin Luo ◽  
Qing Cai ◽  
Xiongfei Zhang
Keyword(s):  
Smart Card ◽  
Route Choice ◽  
Time Constraints ◽  
Peak Period ◽  
Smart Card Data ◽  
Noise Data ◽  
Metro System ◽  
Walking Time ◽  
Operation And Management

The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.

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