Mining temporal and spatial travel regularity for transit planning

Smart Card data from Automated Fare Collection system has been considered as a promising source of information for transit planning. However, literature has been limited to mining travel patterns from transit users and suggesting the potential of using this information. This paper proposes a method for mining spatial regular origins-destinations and temporal habitual travelling time from transit users. These travel regularity are discussed as being useful for transit planning. After reconstructing the travel itineraries, three levels of Density-Based Spatial Clustering of Application with Noise (DBSCAN) have been utilised to retrieve travel regularity of each of each frequent transit users. Analyses of passenger classifications and personal travel time variability estimation are performed as the examples of using travel regularity in transit planning. The methodology introduced in this paper is of interest for transit authorities in planning and management

Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains---all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of $\sim10\%$ leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for analytical modelling when such data are not empirically available, as is often the case.Comment: 10 pages, 5 figures, submitted to International Conference on Computational Science 201

Development of a Common Framework for Analysing Public Transport Smart Card Data

The data generated in public transport systems have proven to be of great importance in improving knowledge of public transport systems, being very valuable in promoting the sustainability of public transport through rational management. However, the analysis of this data involves numerous tasks, so that when the value of analysing the data is finally verified, the effort has already been very great. The management and analysis of the collected data face some difficulties. This is the case of the data collected by the current automated fare collection systems. These systems do not follow any open standards and are not usually designed with a multipurpose nature, so they do not facilitate the data analysis workflow (i.e., acquisition, storage, quality control, integration and quantitative analysis). Intending to reduce this workload, we propose a conceptual framework for analysing data from automated fare collection systems in mobility studies. The main components of this framework are (1) a simple data model, (2) scripts for creating and querying the database and (3) a system for reusing the most useful queries. This framework has been tested in a real public transport consortium in a Spanish region shaped by tourism. The outcomes of this research work could be reused and applied, with a lower initial effort, in other areas that have data recorded by an automated fare collection system but are not sure if it is worth investing in exploiting the data. After this experience, we consider that, even with the legal limitations applicable to the analysis of this type of data, the use of open standards by automated fare collection systems would facilitate the use of this type of data to its full potential. Meanwhile, the use of a common framework may be enough to start analysing the data

Identifying Urban Functional Areas and Their Dynamic Changes in Beijing: Using Multiyear Transit Smart Card Data

A growing number of megacities have been experiencing changes to their landscape due to rapid urbanisation trajectories and travel behaviour dynamics. Therefore, it is of great significance to investigate the distribution and evolution of a city’s urban functional areas over different periods of time. Although the smart card automated fare collection system (SCAFC) is already widely used, few studies have used smart card data to infer information about changes in urban functional areas, particularly in developing countries. Thus, this research aims to delineate the dynamic changes that have occurred in urban functional areas based on passengers’ travel patterns, using Beijing as a case study. We established a Bayesian framework and applied a Gaussian mixture model (GMM) derived from transit smart card data in order to gain insight into passengers’ travel patterns at station level and then identify the dynamic changes in their corresponding urban functional areas. Our results show that Beijing can be clustered into five different functional areas based on the analysis of corresponding transit station functions, namely: multimodal interchange hub and leisure area; residential area; employment area; mixed but mainly residential area; and a mixed residential and employment area. In addition, we found that urban functional areas have experienced slight changes between 2014 and 2017. The findings can be used to inform urban planning strategies designed to tackle urban spatial structure issues, as well as guiding future policy evaluation of urban landscape pattern use

To travel or not to travel: ‘Weather’ is the question. Modelling the effect of local weather conditions on bus ridership

© 2017 The Authors While the influence of weather on public transport performance and ridership has been the topic for some research, the real-time response of transit usage to variations in weather conditions is yet to be fully understood. This paper redresses this gap by modelling the effect that local weather conditions exert on hourly bus ridership in sub-tropical Brisbane, Australia. Drawing on a transit smart card data set and detailed weather measurements, a suite of time-series regression models are computed to capture the concurrent and lagged effects that weather conditions exert on bus ridership. Our findings highlight that changes in particularly temperature and rainfall were found to induce significant hour-to-hour changes in bus ridership, with such effects varying markedly across both a 24 h period and the transit network. These results are important for public transport service operations in their capacity to inform timely responses to real-time changes in passengers’ travel demand induced by the onset of particular weather conditions

Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities

Exploring equity in public transportation planning using smart card data

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

Performance of Public Transport Appraisal using Machine Learning

Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services