scholarly journals Multi-Modal Route Recommender System for Bangkok Public Transportation

2020 ◽  
Vol 8 (6) ◽  
pp. 1380-1384
Keyword(s):  
Travel Time ◽  
Recommender System ◽  
Public Transportation ◽  
User Satisfaction ◽  
Transportation Network ◽  
A Algorithm ◽  
Rail Line ◽  
Experience Survey ◽  
Under Construction ◽  
Modal Route

Currently, Bangkok has a 151 kilometers service of a rail line, whereas the total plan is 540 kilometers. More rail lines are now under construction and supposed to be done by a few years. Regarding a massive public transportation network, we need a route recommender system to make traveling more efficient. This paper proposes the route recommender system which supports multi modes of transportation in Bangkok, including BTS, MRT, ARL, BMTA bus, and Chaophraya Riverboat. Users can see suggested routes and sort routes by travel time, fare, number of transfer, and overall score. The A* algorithm with the Haversine formula as the heuristic function is used to calculate the possible routes. Then the best route is selected based on the score, which is calculated form four factors: travel time, fare, number of transfer, and distance. The database contains 13,510 stops, and the results show that the system can suggest accurate routes within a few seconds, which is fast enough for all use cases and achieved overall user satisfaction at 84.8% from our user experience survey.

Public Transportation Dynamic Guidance Model and Algorithm

2011 ◽  
Vol 368-373 ◽  
pp. 3113-3116
Author(s):  
Liang Zou ◽  
Ling Xiang Zhu
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Computation Time ◽  
A Algorithm ◽  
Travel Costs ◽  
Computational Performance ◽  
Key Factor ◽  
Straight Line Distance ◽  
Average Computation Time ◽  
Guidance Model

The current public transportation guidance models are static and based on travel times, travel distance and travel costs. However latest survey shows that travel time has become the key factor for passenger travel route selection in big cities. Dynamic public transportation guidance model based on travel time and waiting time was proposed and the effectiveness of this model is proved in this paper. To solve this model efficiently, this paper proposed the application of A* algorithm in dealing with this models using straight line distance between two bus stops in electronic maps as Priori knowledge. Finally, the developed model and algorithm were implemented with 50 random OD pairs based on Guangzhou’s public transportation networks (containing 471 public transportation routes and 1040 stops) and Guangzhou’s electronic map. Their computational performance was analyzed experimentally. The result indicates that the models and algorithm proposed in this paper are very efficient. The average computation time of the algorithm proposed in this paper is 0.154s and the average number of nodes selected of this algorithm is 194.2.

scholarly journals Optimal Route Computation for Public Transport with Minimum Travelling Time & Travel Cost: A Case Study of Pokhara City

2019 ◽  
Vol 1 (1) ◽  
pp. 79-86
Author(s):  
R. Thapa ◽  
J.K. Shrestha
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Public Transportation ◽  
Transportation Network ◽  
Optimal Path ◽  
Travel Cost ◽  
Transit Network ◽  
Final Destination ◽  
Travelling Time

In road networks, it is imperative to discover a shortest way to reach the final destination. When an individual is new to a place, lots of time is wasted in finding the destination. With the advancement of technology, various navigation applications have been developed for guiding private vehicles, but few are designed for public transportation. This study is solely concentrated on finding the possible shortest path in terms of minimum time and cost to reach specific destination for an individual. It requires an appropriate algorithm to search the shortest path. With the implementation of Dijkstra’s algorithm, the shortest path with respect to minimum travel time and travel cost was computed. Public transportation network of Pokhara city was taken for the case study of this research. The results of this analysis indicated that when the “time” impedance was used by the algorithm, it generated the shortest path between the origin and destination along with the path to be followed. This study formulates a framework for generating itinerary for passengers in a transit network that allows the user to find the optimal path with minimum travel time and cost.

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.

scholarly journals A Choice Too Far: Transit Difficulty and Early High School Transfer

2020 ◽  
pp. 0013189X2094950 ◽  
Author(s):  
Marc L. Stein ◽  
Julia Burdick-Will ◽  
Jeffrey Grigg
Keyword(s):  
High School ◽  
Travel Time ◽  
Administrative Data ◽  
Public Transportation ◽  
School System ◽  
Ninth Grade ◽  
Public School System ◽  
Baltimore City ◽  
School Transfer ◽  
The Relationship

The challenge of a long and difficult commute to school each day is likely to wear on students, leading some to change schools. We used administrative data from approximately 3,900 students in the Baltimore City Public School System in 2014–2015 to estimate the relationship between travel time on public transportation and school transfer during the ninth grade. We show that students who have relatively more difficult commutes are more likely to transfer than peers in the same school with less difficult commutes. Moreover, we found that when these students change schools, their newly enrolled school is substantially closer to home, requires fewer vehicle transfers, and is less likely to have been included among their initial set of school choices.

scholarly journals DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK

2021 ◽  
Vol 1 ◽  
pp. 1093-1102
Author(s):  
Flore Vallet ◽  
Mostepha Khouadjia ◽  
Ahmed Amrani ◽  
Juliette Pouzet
Keyword(s):  
Decision Making ◽  
Public Transportation ◽  
Transportation Network ◽  
Visual Exploration ◽  
Problem Solution ◽  
Analysis Tool ◽  
Complex Data ◽  
Data Visualisation ◽  
Ex Post ◽  
Passenger Flows

AbstractMassive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

2021 ◽  
pp. 036119812110133
Author(s):  
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Public Transportation ◽  
Gradient Boosting ◽  
Log Data ◽  
Smart Card Data ◽  
Extreme Gradient Boosting ◽  
Express Train ◽  
Total Travel Time ◽  
The Individual

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Fiat Pendolino: Developments, experiences and perspectives

1998 ◽  
Vol 212 (1) ◽  
pp. 7-17 ◽  
Author(s):  
A Elia
Keyword(s):  
Travel Time ◽  
European Countries ◽  
Product Strategy ◽  
Service Experience ◽  
Service Conditions ◽  
Passenger Trains ◽  
Under Construction

The objective in introducing tilting systems for passenger trains is to optimize the service, shortening travel time and improving comfort, while making minimum alterations to existing track layouts and service conditions. The development of the Pendolino system is described in this paper, starting from early tests on bogies and on tilting systems. The author then describes the extensive service experience of Fiat, with more than 50 million train-km produced and 100 trainsets in service or under construction for service in European countries. An outline is presented of current developments and the product strategy, together with a description of available R&D prediction/measuring tools.

scholarly journals Stop Plan of Express and Local Train for Regional Rail Transit Line

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qin Luo ◽  
Yufei Hou ◽  
Wei Li ◽  
Xiongfei Zhang
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Programming Model ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Long Distance ◽  
Passenger Travel ◽  
Urban Rail ◽  
Proposed Model ◽  
Rail Line

The urban rail transit line operating in the express and local train mode can solve the problem of disequilibrium passenger flow and space and meet the rapid arrival demand of long-distance passengers. In this paper, the Logit model is used to analyze the behavior of passengers choosing trains by considering the sensitivity of travel time and travel distance. Then, based on the composition of passenger travel time, an integer programming model for train stop scheme, aimed at minimizing the total passenger travel time, is proposed. Finally, combined with a certain regional rail line in Shenzhen, the plan is solved by genetic algorithm and evaluated through the time benefit, carrying capacity, and energy consumption efficiency. The simulation result shows that although the capacity is reduced by 6 trains, the optimized travel time per person is 10.34 min, and the energy consumption is saved by about 16%, which proves that the proposed model is efficient and feasible.

scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Ordinary Least Squares ◽  
Gap Filling ◽  
Service Levels ◽  
Transit Service ◽  
Logistic Regressions ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

Sign in / Sign up

Export Citation Format

Share Document