John D. Nelson and Steven Wright
John D. Nelson and Tao-Tao Deng
Bus Rapid Transit (BRT) projects in China have experienced rapid growth over a relatively short period, in terms of the number of lines launched and the high quality of infrastructure implemented. Since the first full-featured BRT system was implemented in Beijing in late 2004, BRT schemes have been implemented in 22 cities as one of the key strategies for relieving traffic problems. These systems vary in size, design, service plan, operating features and technology application. The chapter aims to describe and evaluate the physical characteristics, technical performance and operational issues associated with BRT systems as implemented globally and particularly in Chinese cities. A SWOT analysis is applied to identify the key factors that are important to implement BRT in the Chinese context and to identify lessons to be learned.
Wesley M. Cohen, Richard R. Nelson and John P. Walsh
Lisa A. Shay, Woodrow Hartzog, John Nelson and Gregory Conti
Due to recent advances in computerized analysis and robotics, automated law enforcement has become technically feasible. Unfortunately, laws were not created with automated enforcement in mind and even seemingly simple laws have subtle features that require programmers to make assumptions when encoding them. We demonstrate this ambiguity with an experiment where a group of 52 programmers was assigned the task of automating traffic speed limit enforcement. A late-model vehicle was equipped with a sensor that collected actual vehicle speed over a one-hour commute. Each programmer (without collaboration) wrote a program that computed the number of speed limit violations and issued mock tickets. Despite quantitative data for both vehicle speed and the speed limit, the number of tickets issued varied from none to one per sensor sample above the speed limit. Our results from the experiment highlight the significant deviation in number and type of citations issued, based on legal interpretations and assumptions made by programmers without legal training. These deviations were mitigated, but not eliminated, in one sub-group that was provided with a legally reviewed software design specification, providing insight into ways to automate the law in the future. Automation of legal reasoning seems to be the most effective in contexts where legal conclusions are predictable because little room exists for choice in a given model; that is, they are determinable. Yet this experiment demonstrates that even relatively narrow and straightforward “rules” are problematically indeterminate in practice.
Corinne Mulley, John Nelson and David A. Hensher
Intelligent Mobility (IM) links technology in the broadest sense to different aspects of mobility. IM, having developed from the previous focus on Intelligent Transport Systems (ITS), is now associated with the appropriate use of new and emerging technologies linked to the wider societal objective of enabling the smarter, greener and more efficient movement of people and goods. The IM agenda is thus of strong interest to many governments because of its link to future prosperity and quality of life and the way it is seen as promoting ‘joined up thinking’ in a multi-modal world. The chapter illustrates IM in a number of application areas: journey planning, automatic vehicle locationing, bus priority systems, smart parking and smart ticketing. In each case the current research agendas for the application areas are outlined. A section is devoted to Mobility as a Service (MaaS), as a clear IM application in its use of new technology to provide an all-encompassing customer experience for mobility services. This section discusses the current situation with MaaS and identifies the key issues for further research. The concluding section provides overarching suggestions for a research agenda in Intelligent Mobility.
Lisa A. Shay, Woodrow Hartzog, John Nelson, Dominic Larkin and Gregory Conti
The time has come for a cohesive approach to automated law enforcement. The ubiquity of sensors, advances in computerized analysis and robotics, and widespread adoption of networked technologies have paved the way for the combination of sensor systems with law-enforcement algorithms and punishment feedback loops. While in the past, law enforcement was manpower intensive and moderated by the discretion of the police officer on the beat, automated systems scale efficiently, allow meticulous enforcement of the law, provide rapid dispatch of punishment and offer financial incentives to law-enforcement agencies, governments, and purveyors of these systems. Unfortunately, laws were not created with such broad attempts at enforcement in mind and the future portends significant harms to society where many types of violations, particularly minor infractions, can be enforced with unprecedented rigor. This chapter provides a framework for analysis of automated law-enforcement systems that includes a conceptualization of automated law enforcement as the process of automating some or all aspects of surveillance, analysis, and enforcement in an iterative feedback loop. We demonstrate how intended and unintended consequences can result from the automation of any stage in this process and provide a list of issues that must be considered in any automated law enforcement scheme. Those deploying automated law-enforcement schemes should be extremely cautious to ensure that the necessary calculus has been performed and adequate safeguards have been incorporated to minimize the potential for public harm while preserving the benefits of automation.