Towards sustainable transport: wireless detection of passenger trips on public transport buses

An important problem in creating efficient public transport is obtaining data about the set of trips that passengers make, usually referred to as an Origin/Destination (OD) matrix. Obtaining this data is problematic and expensive in general, especially in the case of buses because on-board ticketing systems do not record where and when passengers get off a bus. In this paper we describe a novel and inexpensive system that uses off-the-shelf Bluetooth hardware to accurately record passenger journeys. Here we show how our system can be used to derive passenger OD matrices, and additionally we show how our data can be used to further improve public transport services.Comment: 13 pages, 4 figures, 1 tabl

You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

Improving estimates of migration flows to Eurostat

In this paper we identify the current mandatory requirements and issues concerning the supply of detailed migration data to Eurostat. Using simple illustrations on immigration to the United Kingdom, we show how substantial and significant improvements can be made to the flows reported by the International Passenger Survey, which contain irregularities and missing data due to its relatively small sample size. Our general methodology is based on the idea of smoothing, repairing and combining data within multiplicative component framework

Did Free Wi-Fi Make a Difference to Amtrak’s Capitol Corridor Service? An Evaluation of the Impact on Riders and Ridership

In November 2011, Amtrak launched free Wi-Fi service on all trains traveling the California Capitol Corridor route. Surveys of passengers found that wi-fi access has a positive impact on the expected travel frequency, especially for new riders. A discrete choice model was used to predict the choice to use the free wi-fi service; results identified demographic characteristics and other factors that indicate which passengers are more likely to use the service. Prior to installation, the Capitol Corridor Joint Powers Authority indicated that a 1-2% increase in ridership would justify the free wi-fi. That result appears to have been met and most likely exceeded

Modelling cellphone trace travel mode with neural networks using transit smartcard and home interview survey data

This study proposes a framework to impute travel mode for trips identified from cellphone traces by developing a deep neural network model. In our framework, we use the trips from a home interview survey and transit smartcard data, for which the travel mode is known, to create a set of artificial pseudo-cellphone traces. The generated artificial pseudo-cellphone traces with known mode are then used to train a deep neural network classifier. We further apply the trained model to infer travel modes for the cellphone traces from cellular network data. The empirical case study region is Montevideo, Uruguay, where high-quality data are available for all three types of data used in the analysis: a large dataset of cellphone traces, a large dataset of public transit smartcard transactions, and a small household travel survey. The results can be used to create an enhanced representation of origin-destination trip-making in the region by time of day and travel mode

Detecting Flow Anomalies in Distributed Systems

Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank them using a proposed metric within distributed systems. We evaluate our approach on passengers' records of an urbanized city's public transportation system and correlate our findings with passengers' postings on social media microblogs. Our experiments show that the metric derived using our localization algorithm gives a better ranking of anomalies as compared to standard deviation measures from statistical models. Our case studies also demonstrate that transportation events reported in social media microblogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems

Job-worker spatial dynamics in Beijing: Insights from Smart Card Data.

As a megacity, Beijing has experienced traffic congestion, unaffordable housing issues and jobs-housing im- balance. Recent decades have seen policies and projects aiming at decentralizing urban structure and job-worker patterns, such as subway network expansion, the suburbanization of housing and firms. But it is unclear whether these changes produced a more balanced spatial configuration of jobs and workers. To answer this question, this paper evaluated the ratio of jobs to workers from Smart Card Data at the transit station level and offered a longitudinal study for regular transit commuters. The method identifies the most preferred station around each commuter's workpalce and home location from individual smart datasets according to their travel regularity, then the amounts of jobs and workers around each station are estimated. A year-to-year evolution of job to worker ratios at the station level is conducted. We classify general cases of steepening and flattening job-worker dynamics, and they can be used in the study of other cities. The paper finds that (1) only temporary balance appears around a few stations; (2) job-worker ratios tend to be steepening rather than flattening, influencing commute patterns; (3) the polycentric configuration of Beijing can be seen from the spatial pattern of job centers identified.Authors appreciate Beijing Transport Information Center for data access. This work is financially supported by Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA19040402) and National Natural Science Foundation of China (No. 41701132 and No. 41722103)