Testing the Efficacy of Platform and Train Passenger Boarding, Alighting and Dispersal Through Innovative 3D Agent-Based Modelling Techniques

Urban Rail Transit (2015) 1(2):87–94 DOI 10.1007/s40864-015-0010-0 http://www.urt.cn/ ORIGINAL RESEARCH PAPERS Testing the Efficacy of Platform and Train Passenger Boarding, Alighting and Dispersal Through Innovative 3D Agent-Based Modelling Techniques Selby Coxon1 • Tom Chandler2 • Elliott Wilson2 Received: 13 March 2015 / Revised: 17 May 2015 / Accepted: 26 May 2015 / Published online: 2 September 2015  The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Suburban railways around the world are experiencing a rapid increase in patronage. Higher passenger densities, particularly during peak times of the day, have implications for train punctuality, crowding, accessibility and passenger comfort. Research indicates that the design of the train carriage and the impediments of platform furniture all have an influence on accessibility and passenger dispersal, with consequences for service punctuality and network capacity. Building new concepts in train and station design are expensive undertakings and carry with the investment a high level of risk. Computational simulation methods such as agent-based modelling (ABM) can mitigate this risk at much lower cost. Many contemporary ABM modellers represent passenger flow at a macroscale, often in a single plan view and with agents travelling at same speeds and represented crudely as dots on a flat plane. This paper discusses a body of work concerning the building of a boarding and alighting simulator at a more detailed scale where a deeper and richer experience of crowd behaviour has been modelled using 3D animated figures. The primary benefit of these methods of evaluation is that they take away the expense and lack of realism present in experiments with full-size mock-ups. The outcomes of this work have resulted in sophisticated imagery, underpinned by technical accuracy that provides a tool for & Selby Coxon 1 Faculty of Art Design & Architecture, Monash University, 900 Dandenong Road, Caulfield, Melbourne 3145, Australia 2 Faculty of Information Technology, Monash University, 900 Dandenong Road, Caulfield, Melbourne 3145, Australia Editor: Baoming Han the development of station infrastructure, train carriage design with implications on timetabling and network planning. Keywords Dwell time
Agent-based modelling
3D 1 Introduction Rail is an important contributor to the movement of people and goods in many of the world’s large cities. Suburban, metro and subway systems are very efficient in terms of the number of people moved relative to land use. Rail is a popular means of transport and becoming more so as urban populations increase. In the latter part of the 19th century, when the London Underground opened, only 10 % of the world’s population lived in cities. Now in the early 21st century, over 50 % of the world’s population live in a city [1]. In terms of transit use, 80 % of the population of Tokyo uses the subway, making some 2930 million passenger journeys per year (2009 figure) (Ibid), the highest level of patronage anywhere in the world. Trains are independent of congested road traffic conditions and therefore have the potential to be faster at delivering passengers into city centres. Automation and advances in signalling reduce the impediments to a smooth and timely rail system. The growth in city populations has fuelled increased rail patronage with the consequence that many train networks can struggle to be punctual. The most significant variable in the journey of a train is the time it will take paused at each station. This ‘dwell’ time is at the mercy of how long it takes passengers to board, alight and disperse within the train carriage or across the platform. At peak periods, dwell times can become extended as passengers jostle to board or alight. It is 123 88 general practice that timetables have built-in ‘recovery’ time and attempt to predict extensions of dwell time during peak periods. However, with increased patronage, the predictability of dwell times becomes more difficult [4]. Delayed trains create a number of implications beyond poor punctuality, including the extension of headways (the time gap between services). This extension is especially onerous if the lines are shared with express services and freight trains. Extended dwell times reduce network capacity leading to less services and more late services, ultimately impacting upon an operator’s revenue and contributing to poor passenger perceptions of the mode. Dwell time predictability is important in the creation of service timetables. To this end, operators subdivide the dwell time to better understand where problems lie. Current timetable orthodoxy determines dwell times by mathematical means. While there are variations to the formula, they all in essence treat boarding and alighting as a linear period of time multiplied by a coefficient representative of how much passengers have been slowed down by the circumstances of other passengers, width of the doors and if they are carrying belongings [5]. Accurate calculation of these dwell times will inform operators of the predicted capacity of the network and so drive timetables with some accuracy. However, while building mathematical models might simplify determining dwell times as they may be, they also mask the intricate composition of the causes of extended dwells. Studies show [2] that there is a wide range of qualitative variables that impact upon passenger behaviour while boarding and alighting. These factors include the prevailing culture of the passengers, their age, relative athleticism, the gap between the platform and the train, the level of the occlusion at the door and their motivations once within the train to finding a seat. These human factor variables are difficult to be determined quantitatively, but they do relate strongly to the interface between the passenger and carriage. Figure 1 shows the points between predictable timing with where the unpredictable variation in dwell is located. Figure 2 encapsulates, as a flowchart, each of the ‘factors’ that affect the efficacy of the passenger Fig. 1 Linear diagram of dwell time structure 123 Urban Rail Transit (2015) 1(2):87–94 to board or alight from a train and by implication impact upon the dwell time variability of the service. Extended dwell times imply difficulty in passenger boarding and alighting at anyone or more of the stages outlined above. With significant increases in patronage, particularly during peak time, crowding itself is the significant determinant of extended dwell times. While passengers may not be particularly aware of the wider implications of delays at the station during boarding and alighting, crowding (the cause of the delay) tends to have a greater impact especially upon the perception of comfort. 2 Measurement and Evaluation Methods: The Role of Computational Modelling Historically, methods of determining boarding and alighting performance have be (...truncated)