Algorithm of Estimating Alighting Bus Stops of Smart Card Passengers Based on Trip-Chain

2012 ◽  
Vol 253-255 ◽  
pp. 1918-1921
Author(s):  
Jun Chen ◽  
Zhao Fei Wang
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Estimation Algorithm ◽  
Transportation Systems ◽  
Trip Chain ◽  
Smart Card Data ◽  
Bus Stops ◽  
Public Transportation Planning ◽  
Public Transportation Systems

In order to apply smart card data in decision-making of public transportation planning and management, the paper researched estimating method of alighting bus stops of smart card passengers. Based on Trip-chain thought, the paper presented estimation algorithm applying the three hypotheses of “Next Trip”, “Last Trip” and “Return Trip”. Then, the algorithm was tested and analyzed using large-scale actual data of Advanced Public Transportation Systems of Nanning City in China. The results show that Trip-chain Method can estimate majority of alighting bus stops.

scholarly journals Exploring Equity in Public Transportation Planning Using Smart Card Data

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3039
Author(s):  
Kiarash Ghasemlou ◽  
Murat Ergun ◽  
Nima Dadashzadeh
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Transportation Planning ◽  
Demand Characteristic ◽  
Monthly Data ◽  
Smart Card Data ◽  
Planning Methods ◽  
Public Transportation Planning ◽  
Daily Travel ◽  
A Share

Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.

Public Transportation OD Calculation Based on Trip Chain

2013 ◽  
Vol 401-403 ◽  
pp. 2151-2154
Author(s):  
Lai Ping Luo ◽  
Jing Zhang
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Bus Stop ◽  
Trip Chain ◽  
Smart Card Data ◽  
Card System

Public transportation smart card system in China has been widely used in many cities recently. Large amounts of information implicit in the smart card, but it is not completely applied, because the information is incomplete, such as the information of getting-off bus stop. For this problem, a method is proposed to calculate OD (Origin-Destination) of smart card data. And it is well applied in digging the information of getting-off bus stop.

Transit Travel Demand Analysis Method Based on APTS Multi-Source Data

2013 ◽  
Vol 361-363 ◽  
pp. 2122-2126
Author(s):  
Jun Chen ◽  
Xiao Hua Li ◽  
Lan Ma
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Travel Demand ◽  
Low Cost ◽  
Transportation Systems ◽  
Demand Analysis ◽  
Sample Survey ◽  
Analysis Method ◽  
Source Data ◽  
Bus Stops

Traditional transit travel information is acquired by Trip Sample Survey which has some disadvantages including high cost and short data lifecycle. This paper researched transit travel demand analysis method using Advanced Public Transportation Systems (APTS) data. The study collected APTS data of Nanning City in China and established APTS multi-source data analysis platform applying data warehouse technology. Based on key problems research, the paper presented the analysis procedure and content. Then, this study proposed the core algorithms of the method which are determinations of boarding bus stops, alighting bus stops and transfer bus stops of smart card passengers. Finally, these algorithms programs are experimented using large scale practical APTS data. The results show that this analysis method is low cost, operability and high accuracy.

scholarly journals Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

2015 ◽  
Vol 112 (18) ◽  
pp. 5643-5648 ◽  
Author(s):  
Ricardo Silva ◽  
Soong Moon Kang ◽  
Edoardo M. Airoldi
Keyword(s):  
Public Transportation ◽  
Transportation Systems ◽  
Smart Cards ◽  
Natural State ◽  
Smart Card Data ◽  
Traffic Volumes ◽  
Massive Scale ◽  
Public Transportation Systems ◽  
Passenger Behavior ◽  
Alternative Solutions

Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.

scholarly journals Passenger Travel Regularity Analysis Based on a Large Scale Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qi Ouyang ◽  
Yongbo Lv ◽  
Yuan Ren ◽  
Jihui Ma ◽  
Jing Li
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Large Scale ◽  
Spatial Clustering ◽  
Reconstruction Algorithm ◽  
Travel Patterns ◽  
Node Importance ◽  
The People ◽  
Passenger Travel ◽  
Smart Card Data

Analysis of passenger travel habits is always an important item in traffic field. However, passenger travel patterns can only be watched through a period time, and a lot of people travel by public transportation in big cities like Beijing daily, which leads to large-scale data and difficult operation. Using SPARK platform, this paper proposes a trip reconstruction algorithm and adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel patterns of each Smart Card (SC) user in Beijing. For the phenomenon that passengers swipe cards before arriving to avoid the crowd caused by the people of the same destination, the algorithm based on passenger travel frequent items is adopted to guarantee the accuracy of spatial regular patterns. At last, this paper puts forward a model based on density and node importance to gather bus stations. The transportation connection between areas formed by these bus stations can be seen with the help of SC data. We hope that this research will contribute to further studies.

Accessibility, Affordability, and Addressing Informal Services in Bus Reform: Lessons from Bogotá, Colombia

2017 ◽  
Vol 2634 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Camila Rodriguez ◽  
Tatiana Peralta-Quirós ◽  
Luis A. Guzman ◽  
Sebastian A. Cárdenas Reyes
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Developing World ◽  
Public Transit ◽  
Transportation Systems ◽  
Reform Process ◽  
Public Transport System ◽  
Bus Services ◽  
Public Transportation Systems ◽  
The City

Many cities in the developing world are reforming transit by formalizing bus services to capture user and nonuser benefits. A forerunner in transit reform, the city of Bogotá, Colombia, first implemented the TransMilenio bus rapid transit (BRT) system and then more recently undertook a large-scale initiative to formalize and regulate traditional urban buses in the city. This integrated public transport system [Sistema Integrado de Transporte Público (SITP)] is transforming Bogotá’s traditional urban bus system into a regulated, concessionary system with restructured bus routes that integrates operations, fares, and infrastructure with the TransMilenio BRT. An investigation was conducted to determine whether the SITP has increased affordability and employment accessibility for public transit users in Bogotá. Results revealed that most accessibility improvements resulted from the recent expansion (and significantly higher speeds) of TransMilenio rather than the SITP. Results of an analysis conducted with budget constraints to determine affordable accessibility indicated that employment accessibility was reduced overall; however, the new integrated fare increased accessibility over traditional buses, especially on the periphery and in southern areas of the city, as a result of reduced transfer costs. Overall, results partly explain the lack of enthusiasm for the bus reform process on the part of public transit users and the political discomfort that becomes apparent when embarking on this process in developing-world cities. Also, more incremental, flexible reform might be crafted for public transportation systems that are dominated by informal services.

scholarly journals Joint Frequency-Setting and Pricing Optimization on Multimodal Transit Networks at Scale

2020 ◽  
Vol 54 (3) ◽  
pp. 839-853
Author(s):  
Dimitris Bertsimas ◽  
Yee Sian Ng ◽  
Julia Yan
Keyword(s):  
Public Transportation ◽  
Large Scale ◽  
Wait Time ◽  
Real Data ◽  
Transportation Systems ◽  
Budget Constraints ◽  
Transit Networks ◽  
Joint Frequency ◽  
Public Transportation Systems ◽  
Pricing Optimization

Modern public transportation systems are increasingly complex: they are operated on a large scale, must support booming urban populations, and run under tight budget constraints. Additionally, passengers are able to make choices between a variety of commuting options. We develop formulations for minimizing system wait time in multimodal networks, while accounting for operator budget constraints, capacity constraints, and passenger preferences. Furthermore, our algorithms run to near optimality in minutes for city-sized networks. We demonstrate the benefit of setting schedule frequencies and prices jointly through case studies on real data from Boston and Tokyo. To our knowledge, ours is the first paper that addresses joint frequency-setting and pricing optimization for public transit networks and at scale.

scholarly journals Virus Transmission Risk in Urban Rail Systems: Microscopic Simulation-Based Analysis of Spatio-Temporal Characteristics

2021 ◽  
pp. 036119812110101
Author(s):  
Jiali Zhou ◽  
Haris N. Koutsopoulos
Keyword(s):  
Public Transportation ◽  
Virus Transmission ◽  
Flow Characteristics ◽  
Transportation Systems ◽  
Transmission Risk ◽  
Microscopic Simulation ◽  
Spatial And Temporal Patterns ◽  
Subway System ◽  
Public Transportation Systems ◽  
The Impact

The transmission risk of airborne diseases in public transportation systems is a concern. This paper proposes a modified Wells-Riley model for risk analysis in public transportation systems to capture the passenger flow characteristics, including spatial and temporal patterns, in the number of boarding and alighting passengers, and in number of infectors. The model is used to assess overall risk as a function of origin–destination flows, actual operations, and factors such as mask-wearing and ventilation. The model is integrated with a microscopic simulation model of subway operations (SimMETRO). Using actual data from a subway system, a case study explores the impact of different factors on transmission risk, including mask-wearing, ventilation rates, infectiousness levels of disease, and carrier rates. In general, mask-wearing and ventilation are effective under various demand levels, infectiousness levels, and carrier rates. Mask-wearing is more effective in mitigating risks. Impacts from operations and service frequency are also evaluated, emphasizing the importance of maintaining reliable, frequent operations in lowering transmission risks. Risk spatial patterns are also explored, highlighting locations of higher risk.

Sign in / Sign up

Export Citation Format

Share Document