Andrew Hamilton

The evolution of urban traffic control: changing policy and technology

Abstract The history of urban traffic control (UTC) throughout the past century has been a continued race to keep pace with ever more complex policy objectives and consistently increasing vehicle demand. Many benefits can be observed from an efficient UTC system, such as reduced congestion, increased economic efficiency and improved road safety and air quality. There have been significant advances in vehicle detection and communication technologies which have enabled a series of step changes in the capabilities of UTC systems, from early (fixed time) signal plans to modern integrated systems. A variety of UTC systems have been implemented throughout the world, each with individual strengths and weaknesses; this paper seeks to compare the leading commercial systems (and some less well known systems) to highlight the key characteristics and differences before assessing whether the current UTC systems are capable of meeting modern transport policy obligations and desires. This paper then moves on to consider current and future transport policy and the technological landscape in which UTC will need to operate over the coming decades, where technological advancements are expected to move UTC from an era of limited data availability to an era of data abundance.

Performance Evaluation of Stage Skipping and New Data Sources Compared against MOVA Control

This research describes a novel Delay Minimisation Algorithm (DEMA) for traffic signal control, which operates without a predetermined stage order. The paper includes a technical review of the problems surrounding a more flexible system compared to the traditional `cycle based approach. Applying DEMA to a case study intersection (currently controlled by MOVA) resulted in statistically significant improvements in performance across a range of demand scenarios. During congested conditions, there was a reduction of 5.62% in mean delay and up to a 22.17% reduction during lower demand scenarios. The mean journey time also reduced, ranging from a 3.52% to a 24.01% reduction.

Human Perceptions of Vehicle Turning Intention: Overall Performance and Contributory Factors

All pedestrians, drivers, and cyclists regularly make predictions about where they think an oncoming vehicle is intending to travel so that they can successfully and safely navigate road systems. Despite the importance of these predictions, the effectiveness of this process is currently poorly understood, as all existing research has been focused on predictions from in-vehicle technologies. This study investigated how well observers were able to predict a vehicles turning intention as it approached an intersection. Logistic regression analysis was used to explore the explanatory variables involved in the success of this process. An interactive touch screen experiment was developed, and more than 100 participants attempted to predict the turning intention of several vehicles. The participants were very good overall at predicting turning intention, with a median success rate of approximately 90% when the vehicle was 0 to 20 m (0 to 21.9 yd) away from the intersection; however, the median success rate fell substantially to approximately 70% when the vehicle was 30 to 50 m (32.8 to 54.7 yd) away. Other key explanatory variables included both vehicle-specific factors (e.g., use of indicator lights) and, crucially, the intersection layout, which together provided valuable information on the relationship between intersection design and road safety.

Lessons from proving ground experiments to investigate junction control

An experiment was conducted using the InnovITS proving ground in Nuneaton. Thirty cars with volunteer drivers were asked to drive around a tight closed road circuit causing them to pass repeatedly through a cross-roads junction from all directions. The junction was signalized. In different test-runs of the experiment the traffic lights were controlled by either an automated fixed-time system or by a human using remote control. All vehicles in the test were instrumented using GPS and bluetooth. Video footage from two cameras was also recorded.recorded. The goal of the experiment was to collect data on the performance of human junction controllers. This was motivated by earlier work indicated that human controllers could perform well at this task in a simulated `computer game environment. In particular this paper examines some of the issues that arise when trying to simulate an urban road junction in this manner. For example results are presented indicating differences in network performance depending on whether the drivers were instructed to follow a fixed route or a random route of their choice. Thus providing some guidance for maximizing the fidelity of this type of simulation in the future. The paper also presents a detailed analysis of the sensor data and video footage to measure the performance of the junction under the different modes of control.