Urban Services on Regional Rail: Local and Network-Based Methodologies for Evaluating New Stations in Toronto
Becca Nagorsky, MetrolinxShow Abstract
Richard Borbridge, Metrolinx
The Greater Toronto and Hamilton Area’s regional GO Rail network is undergoing a substantive upgrade to provide two-way, all-day, electrified, 15-minute service. These investments have led to discussions about the nature of the service and calls to add new stations, particularly within the City of Toronto. This paper explores the methodology used to evaluate specific new station locations in the City of Toronto and beyond, along with a concurrent analysis of the impacts of a package of new stations on the network performance and hierarchy. Both assessments used a multi-faceted business case framework. Travel-time impacts, land use, costs, feasibility, and other factors influenced the recommendation of sites intended to optimize the substantial investment in network infrastructure, and address city-building objectives. Network analysis points to new stations within the City shifting the traditional three-tier network hierarchy comprised of regional rail, urban rapid transit, and local transit to a hybrid network where regional rail serves some intermediate functions.
Estimating the Most Likely Space-Time Path by Mining Automatic Fare Collection Data
Chen Xing, Beijing Jiaotong UniversityShow Abstract
Leishan Zhou, Beijing Jiaotong University
Jinjin Tang, Beijing Jiaotong University
Zhou Hanxiao, Beijing Jiaotong University
Automatic Fare Collection (AFC) systems record the time and location information when a passenger enters or leaves the urban rail transit system by swiping his/her smart card. The AFC transaction data records passengers’ trip information exactly and can be used for many researches. This paper aims to estimate the most likely space-time path based on AFC transaction data. A complete passenger’s travel path is consisted of four components: access walk, in-vehicle, egress walk and transfer walk. By constructing a time-extended network based on train timetable data, a space-time path model is formulated to simulate passenger’s trajectory, which tells a passenger’s movements among activity locations with respect to time. Then, a time-dependent maximum likelihood space-time estimation model is proposed to estimate the most likely space-time path for all passengers. Considering the computational efficiency and the characteristic of space-time path, we propose an improved Dijkstra algorithm to solve the space-time path estimation problem. Real-world AFC transaction data and train timetable data from Xi’an subway is used to verify the proposed model and algorithm.
Operational Impacts of Platform Doors in Metros
Alexander Barron, Imperial College LondonShow Abstract
Shane Canavan, Imperial College London
Richard Anderson, Imperial College London
Judith Cohen, Transport for London (TFL)
Platform doors are increasingly installed by metros, primarily to improve safety. However, they have the potential for both positive and negative operational impacts, mostly by affecting dwell times at stations. Using data from the CoMET and Nova international metro benchmarking consortia of 33 metro systems, this paper seeks to understand and quantify these operational impacts.
Overall, platform doors have a net negative impact on dwell times, leading to between 4 and 15 seconds of extra time per station stop. This is due to additional time for the larger doors to open and close, slower passenger movements due to the additional distance between platforms and trains, and, most importantly, extended departure delays after both sets of doors are closed caused by the need to ensure safety (that no one is trapped in the gap between the two sets of doors). This is a particular problem in mainland China, where metros conduct manual safety checks that require drivers to step out of trains onto platforms.
However, despite longer dwell times, platform doors have a net positive impact on metro operations, largely due to the many safety benefits that also reduce delays and thereby improve service performance. There are also potential benefits regarding energy and ventilation. To mitigate the negative impacts, metros should seek to refine procedures and improve technology to reduce dwell time delays caused by platform doors. Reducing or eliminating these extra delays are essential to delivering efficient service and maximum capacity, provided that safety can be assured.
Empirical Analysis of Traveling Backwards and Passenger Flows Reassignment on a Metro Network with AFC Data and Train Diagram
Yanan Li, Tongji UniversityShow Abstract
Wei Zhu, Tongji University
Ruihua Xu, Tongji University
In recent years, the metro system in China is becoming overcrowded in some mega-cities such as Beijing, Shanghai, Guangzhou, etc. As many passengers fail to board on trains in an overcrowded metro network during peak periods, some of them are willing to spend more time and energy traveling backwards to secure a seat or even room for standing. Traditional studies on travel behavior analysis and transit assignment models seldom deal with this situation. We propose a methodology for identifying the phenomenon of “traveling backwards” (TB) and reassigning passenger flows on a metro network with automatic fare collection (AFC) data and actual train diagram. As a numerical example, this integrated approach is applied to the Beijing metro system. Our research shows that affinity propagation cluster method with BWP index for TB identification works well and traveling backwards model (TBM) for reassignment is a good replenishment for the existing assignment model, especially for those mega-cities’ networks in rush hours.
Decomposing Journey Times on Mass Transit Systems via Semiparametric Mixed Methods
Ramandeep Singh, Imperial College LondonShow Abstract
Daniel Graham, Imperial College London
Richard Anderson, Imperial College London
In this paper, we propose a Bayesian assignment algorithm to assign passenger trips to trains, using the London Underground metro system as a case study. Using the results from the assignment, we decompose journey times of trips into parts, and undertake a semiparametric regression analysis to quantify the effect of various operational and physical characteristics on journey time and journey time variance. We apply a random effects model structure, and present rankings of journey time performance at a line and station level. The analysis methods presented here have been developed for ease of application for operators, using a combination of readily available automated fare collection (AFC) and automatic vehicle location (AVL) data. The results from the assignment and regression models can be aggregated at line and system level, and can be used in the future to benchmark journey time performance across lines, and more generally, across metro systems.