Bus travel time modelling using GPS probe and smart card data: A probabilistic approach considering link travel time and station dwell time

2018 ◽  
Vol 23 (2) ◽  
pp. 175-190 ◽  
Author(s):  
Zhuang Dai ◽  
Xiaolei Ma ◽  
Xi Chen
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Dwell Time ◽  
Probabilistic Approach ◽  
Smart Card Data ◽  
Link Travel Time ◽  
Bus Travel Time

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.

scholarly journals Operating Time Division for a Bus Route Based on the Recovery of GPS Data

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Jian Wang ◽  
Yang Cao
Keyword(s):  
Travel Time ◽  
Operating Time ◽  
Time Of Day ◽  
Operating Conditions ◽  
Gps Data ◽  
Recovery Method ◽  
Speed Up ◽  
Link Travel Time ◽  
Partition Method ◽  
Bus Travel Time

Bus travel time is an important source of data for time of day partition of the bus route. However, in practice, a bus driver may deliberately speed up or slow down on route so as to follow the predetermined timetable. The raw GPS data collected by the GPS device equipped on the bus, as a result, cannot reflect its real operating conditions. To address this concern, this study first develops a method to identify whether there is deliberate speed-up or slow-down movement of a bus. Building upon the relationships between the intersection delay, link travel time, and traffic flow, a recovery method is established for calculating the real bus travel time. Using the dwell time at each stop and the recovered travel time between each of them as the division indexes, a sequential clustering-based time of day partition method is proposed. The effectiveness of the developed method is demonstrated using the data of bus route 63 in Harbin, China. Results show that the partition method can help bus enterprises to design reasonable time of day intervals and significantly improve their level of service.

Modeling Bus Travel Time Reliability with Supply and Demand Data from Automatic Vehicle Location and Smart Card Systems

2015 ◽  
Vol 2533 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Zhen-Liang Ma ◽  
Luis Ferreira ◽  
Mahmoud Mesbah ◽  
Ahmad Tavassoli Hojati
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Supply And Demand ◽  
Seemingly Unrelated Regression ◽  
Regression Equations ◽  
Travel Time Reliability ◽  
Automatic Vehicle Location ◽  
Different Types ◽  
Vehicle Location ◽  
Bus Travel Time

Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads.

Estimation of Platform Waiting Time Distribution Considering Service Reliability Based on Smart Card Data and Performance Reports

2017 ◽  
Vol 2652 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Amr M. Wahaballa ◽  
Fumitaka Kurauchi ◽  
Toshiyuki Yamamoto ◽  
Jan-Dirk Schmöcker
Keyword(s):  
Travel Time ◽  
Waiting Time ◽  
Smart Card ◽  
Time Distribution ◽  
Goodness Of Fit ◽  
Likelihood Method ◽  
Service Reliability ◽  
Waiting Time Distribution ◽  
Smart Card Data ◽  
Performance Reports

The estimation of platform waiting time has so far received little attention. This research aimed to estimate platform waiting time distributions on the London Underground, considering travel time variability by using smart card data that were supplemented by performance reports. The on-train and ticket gate to platform walking times were assumed to be normally distributed and were matched with the trip time recorded by the smart cards to estimate the platform waiting time distribution. The stochastic frontier model was used, and its parameters were estimated by the maximum likelihood method. The cost frontier function was used to represent the relation between the travel time recorded in the smart card data as an output and the on-train time and walking time between the ticket gate and the platform as inputs. All estimated parameters were statistically significant, as shown by p-values. Comparing the travel time values estimated by the proposed model with the times recorded recorded in the smart card data shows a goodness-of-fit coefficient of determination of more than 95%. The estimation proved to have quick convergence and was computationally efficient. The results could facilitate improvements in transit service reliability analysis and passenger flow assignment. Matching the obtained distributions with the observed smart card data will help with estimating route choice behavior that can validate current transit assignment models.

scholarly journals Measurement of Opportunity Cost of Travel Time for Predicting Future Residential Mobility Based on the Smart Card Data of Public Transportation

2018 ◽  
Author(s):  
Takashi Nicholas Maeda ◽  
Junichiro Mori ◽  
Masanao Ochi ◽  
Tetsuo Sakimoto ◽  
Ichiro Sakata
Keyword(s):  
Urban Development ◽  
Travel Time ◽  
Residential Mobility ◽  
Smart Card ◽  
Public Transportation ◽  
Opportunity Cost ◽  
Travel Cost Method ◽  
Causal Influence ◽  
Mobility Data ◽  
Smart Card Data

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

Development of Estimating Methodology for Transit Accessibility Using Smart Card Data

2021 ◽  
pp. 036119812110275
Author(s):  
Hyunsoo Yun ◽  
Eun Hak Lee ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho
Keyword(s):  
Spatial Distribution ◽  
Travel Time ◽  
Smart Card ◽  
Explanatory Variable ◽  
Travel Information ◽  
Transit Network ◽  
Important Index ◽  
Transit Accessibility ◽  
Smart Card Data ◽  
Two Factors

Transit accessibility is an explanatory variable evaluating the mobility of a region in consideration of the connectivity and demand among the regions, which has been used for an important index to determine transport policy on the transit network. This study aims to develop an accessibility index considering the two factors with a demand-weighted approach, that is, impedance and attraction level. Two variables, travel time and the ratio of trips, are employed to calculate the accessibility index, and comparative assessments between zones are conducted. The application of smart card data makes it possible to analyze travel information and reflect them empirically in the model. This study identifies zones with vulnerable accessibility and suggests criteria for transit investment plans with two aspects, that is, intensive transit area and spatial distribution of the accessibility index. These aspects contribute to transit planners by suggesting transit investment criteria and comprehensible statistics to evaluate accessibility. Since zones with low accessibility indexes are identified as being vulnerable to access from other zones, policymakers should focus on those zones to improve the overall transit network.

scholarly journals Optimized Skip-Stop Metro Line Operation Using Smart Card Data

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Peitong Zhang ◽  
Zhanbo Sun ◽  
Xiaobo Liu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Low Cost ◽  
Optimal Solution ◽  
Cost Savings ◽  
Passenger Demand ◽  
Passenger Travel ◽  
Smart Card Data ◽  
Transit Agencies ◽  
The Right

Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional “A/B” scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

scholarly journals Impact of a New Metro Line: Analysis of Metro Passenger Flow and Travel Time Based on Smart Card Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiao Fu ◽  
Yu Gu
Keyword(s):  
Travel Time ◽  
Smart Card ◽  
Rapid Development ◽  
Flow Distribution ◽  
Urban Rail Transit ◽  
Mobility Patterns ◽  
Passenger Flow ◽  
Smart Card Data ◽  
The Impact ◽  
Metro Network

Over the past few decades, massive volumes of smart card data from metro systems have been used to investigate passengers’ mobility patterns and assess the performance of metro network. With the rapid development of urban rail transit in densely populated areas, new metro lines are constantly designed and operated in recent years. The appearance of new metro lines may significantly affect passenger flow and travel time in the metro network. In this study, smart card data of metro system from Nanjing, China, are used to study the changes of metro passenger flow and travel time due to the operation of a new metro line (i.e., Line 4, opened on 18 January 2017). The impact of the new metro line on passenger flow distribution and travel time in the metro network is first analysed. As commuting is one of the major purposes of metro trips, the impact of the new metro line on commuters’ trips is then explicitly investigated. The results show that the new metro line influences passenger flow, travel time, and travel time reliability in the metro network and has different impacts on different categories of commuters.

Sign in / Sign up

Export Citation Format

Share Document