Deriving Public Transportation Timetables with Large-Scale Cell Phone Data

AbstractIn this paper, we propose an approach to deriving public transportation timetables of a region (i.e. country) based on (i) large- scale, non-GPS cell phone data and (ii) a dataset containing geographic information of public transportation stations. The presented algorithm is designed to work with movements data, which are scarce and have a low spatial accuracy but exists in vast amounts (large-scale). Since only aggregated statistics are used, our algorithm copes well with anonymized data. Our evaluation shows that 89% of the departure times of popular train connections are correctly recalled with an allowed deviation of 5minutes. The timetable can be used as feature for transportation mode detection to separate public from private transport when no public timetable is available

User perspectives in public transport timetable optimisation

The present paper deals with timetable optimisation from the perspective of minimising the waiting time experienced by passengers when transferring either to or from a bus. Due to its inherent complexity, this bi-level minimisation problem is extremely difficult to solve mathematically, since timetable optimisation is a non-linear non-convex mixed integer problem, with passenger flows defined by the route choice model, whereas the route choice model is a non-linear non-continuous mapping of the timetable. Therefore, a heuristic solution approach is developed in this paper, based on the idea of varying and optimising the offset of the bus lines. Varying the offset for a bus line impacts the waiting time passengers experience at any transfer stop on the bus line.In the bi-level timetable optimisation problem, the lower level is a transit assignment calculation yielding passengers' route choice. This is used as weight when minimising waiting time by applying a Tabu Search algorithm to adapt the offset values for bus lines. The updated timetable then serves as input in the following transit assignment calculation. The process continues until convergence.The heuristic solution approach was applied on the large-scale public transport network in Denmark. The timetable optimisation approach yielded a yearly reduction in weighted waiting time equivalent to approximately 45 million Danish kroner (9 million USD)

Utilizing Call Detail Records for Travel Mode Discovery in Urban Areas

Mobile network operators often bill their customers based on their network usage. For this purpose, operators collect information about billable events, such as calls, text messages, and data usage. In recent years, operators have realized that they can monetize these billing records by selling insights extracted from them. In this thesis, a multi-stage data analysis algorithm is presented that uses these billing records for travel mode classification. This algorithm identifies whether a mobile phone user has traveled using a public transportation bus or using another transportation mode. The billing records collected by a network operator contain the time at which a billable event happened, as well as the network cell from which the event originated. The coverage area of each network cell is known to the operator. Therefore, the billing records of a mobile phone user give an overview of that user’s approximate location at different times. This data can be used to discover the sequence of network cells that the user has traveled through during a trip. Travel mode classification algorithms in literature analyze long-distance or medium- distance trips. The data analysis algorithm presented in this thesis is novel for analyzing and classifying short-distance, intra-city trips. To classify mobility traces, it uses publicly available bus timetable data and road network infrastructure data. The accuracy of the classification algorithm is evaluated using a two-fold cross-validation analysis

Examining the spatial congruence between data obtained with a novel activity location questionnaire, continuous GPS tracking, and prompted recall surveys.

BACKGROUND: Place and health researchers are increasingly interested in integrating individuals' mobility and the experience they have with multiple settings in their studies. In practice, however, few tools exist which allow for rapid and accurate gathering of detailed information on the geographic location of places where people regularly undertake activities. We describe the development and validation of a new activity location questionnaire which can be useful in accounting for multiple environmental influences in large population health investigations. METHODS: To develop the questionnaire, we relied on a literature review of similar data collection tools and on results of a pilot study wherein we explored content validity, test-retest reliability, and face validity. To estimate convergent validity, we used data from a study of users of a public bicycle share program conducted in Montreal, Canada in 2011. We examined the spatial congruence between questionnaire data and data from three other sources: 1) one-week GPS tracks; 2) activity locations extracted from the GPS tracks; and 3) a prompted recall survey of locations visited during the day. Proximity and convex hull measures were used to compare questionnaire-derived data and GPS and prompted recall survey data. RESULTS: In the sample, 75% of questionnaire-reported activity locations were located within 400 meters of an activity location recorded on the GPS track or through the prompted recall survey. Results from convex hull analyses suggested questionnaire activity locations were more concentrated in space than GPS or prompted-recall locations. CONCLUSIONS: The new questionnaire has high convergent validity and can be used to accurately collect data on regular activity spaces in terms of locations regularly visited. The methods, measures, and findings presented provide new material to further study mobility in place and health research

Data-Driven Optimization Models for Feeder Bus Network Design

Urbanization is not a modern phenomenon. However, it is worthwhile to note that the world urban population growth curve has up till recently followed a quadratic-hyperbolic pattern (Korotayey and Khaltourina, 2006). As cities become larger and their population expand, large and growing metropolises have to face the enormous traffic demand. To alleviate the increasing traffic congestion, public transit has been considered as the ideal solution to such troubles and problems restricting urban development. The metro is a type of efficient, dependable and high-capacity public transport adapted in metropolises worldwide. At the same time, the residents from crowded cities migrated to the suburban since 1950s. Such sub-urbanization brings more decentralized travel demands and has challenged to the public transit system. Even the metro lines are extended from inner city to outer city, the commuters living in suburban still have difficulty to get to the rail station due to the limited transportation resources. It is becoming inevitable to develop the regional transit network such as feeder bus that picks up the passengers from various locations and transfer them to the metro stations or transportation hubs. The feeder bus will greatly improve the efficiency of metro stations whose service area in the suburban area is usually limited. Therefore, how to develop a well-integrated feeder system is becoming an important task to planners and engineers. Realizing the above critical issues, the dissertation focus on the feeder bus network design problem (FBNDP) and contributes to three main parts: 1. Develop a data-mining strategy to retrieve OD pair from the large scale of the cellphone data. The OD pairs are able to present the users’ daily behaver including the location of residence, workplace with the timestamp of each trip. The spatial distribution of urban rail transit user demand from the OD pair will help to support the establishment and optimization of the feeder bus network. The dissertation details the procedure of data acquisition and utilization. The machine leaning is applied to predict the travel demand in the future. 2. Present a mathematical model to design the appropriate service area and routing plans for a flexible feeder transit. The proposed model features in utilizing the real-world data input and simultaneously selecting bus stops and designing the route from those targeted stops to urban rail stops. 3. Propose an improved feeder bus network design model to provide precise service to the commuters. Considering the commuters are time-sensitive during the peak hours, the time-windows of each demand is taken in to account when generating the routes and the schedule of feeder bus system. The model aims to pick up the demand within the time-windows of the commuters’ departure time and drop off them within the reasonable time. The commuters will benefit from the shorter waiting time, shorter walking distance and efficient transfer timetable