This paper reports on results of a study undertaken in the U.K. aimed at investigating factors affecting the car following process. An understanding of the factors affecting this complex decision making process is essential to a wide range of theoretical issues including driver workload, capacity and the modeling of freeway flow, as well as practical applications such as the design of in-vehicle driver aids and assistance systems, many of which have direct relevance to increasing driver safety. The study used an instrumented vehicle to collect time dependent following data for a group of test drivers. Data was collected on two differing types of high speed road, using six primary subjects who drove a test vehicle, supplemented by data on one hundred and twenty three drivers that were observed following the test vehicle. Examination was made of how the time headway chosen by a driver is influenced by a range of situational variables commonly believed to effect behavior, with four main findings. Firstly, headway was found to change according to the type of vehicle being followed (i.e. subjects followed closer to trucks than to cars), secondly, little variation was found with changes in overall traffic flow, thirdly, little correlation was found with road type, and lastly a distinct day-to-day variation in individual behavior was observed.

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 urban traffic control system, such as reduced congestion, increased economic efficiency and improved road safety and air quality.There have been significant advances in vehicle detection and communications 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.

Wireless Power Transfer (WPT) offers a viable means of charging Electric Vehicles (EV)'s whilst in a dynamic state (DWPT), mitigating issues concerning vehicle range, the size of on-board energy storage and the network distribution of static based charging systems. Such Charge While Driving (CWD) technology has the capability to accelerate EV market penetration through increasing user convenience, reducing EV costs and increasing driving range indefinitely, dependent upon sufficient charging infrastructure. This paper reviews current traction battery technologies, conductive and inductive charging processes, influential parameters specific to the dynamic charging state as well as highlighting notable work undertaken within the field of WPT charging systems. DWPT system requirements, specific to the driver, vehicle and infrastructure interaction environment are summarised and international standards highlighted in order to acknowledge the work that must be done within this area. It is important to recognise that the gap is not currently technological; instead, it is an implementation issue. Without the necessary standardisation, system architectures cannot be developed and implemented without fear of interoperability issues between countries or indeed systems. For successful deployment, the technologies impact should be maximised with the minimum quantity of infrastructure and technology use, deployment scenarios and locations are discussed that have the potential to bring this to fruition. IntroductionThe electrification of road transport provides a viable means of reducing fossil fuel consumption and environmental pollution, hence the recent advancements in Electric Vehicle (EV) design and performance [1]. However, the high costs and poor specific energy densities of batteries compared to fossil fuels results in a less than ideal scenario [2]. Due to their relatively shorter range, EV's require more frequent charging (than refuelling of Internal Combustion Engine (ICE) vehicles) to maintain a desirable range and with long charge times (compared with conventional refuelling times) or potential battery degradation that occurs during rapid charging; battery charging technology has restricted EV development. With no significant advancements in battery technology that would bring EV range in line with comparable ICE vehicles forecasted within the foreseeable future [3] this has resulted in substantial research into alternative charging methods. Whilst conventional plug in charging is the most common form, there are still conductive energy losses within the system resulting in an overall efficiency of around 86% [4] and potentially lower for rapid chargers. In addition, the high power transfer, human handling and the ability for the user to forget to plug in/out result in a pragmatic scenario.Wireless Power Transfer (WPT) technology is capable of mitigating the issues of plug in charging; the EV is parked over a coil that inductively transfers electrical energy to a receiver coil positioned on the vehicle. ...

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Policies to reduce levels of traffic congestion and pollution in major urban areas often focus strongly on the concept of a sustainable transport system, but to achieve this vision a significant modal shift from private car to public transport will be required. This paper reports on a recent research study which provides a framework within which to model the behavioral responses of travelers following the implementation of strong bus priority measures (where road capacity is deliberately removed from general traffic and given to buses). A summary of the different behavioral responses which can be expected is given and results from a practical implementation of the framework which has been based on two commercial transport modeling packages (CONTRAM and TRIPS) are discussed. These results suggest firstly that the effect of implementing such strong bus priority measures is as dependent on the characteristics of the local travelers as on the scheme itself and secondly that implementing too strong a scheme may not benefit public transport overall.

Parking Guidance and Information (PGI) signs are thought to enable a more efficient use of the available parking stock. Despite the installation of PGI systems in many cities and their operation for a number of years, there is a lack of reliable evidence of the size of the benefits that these systems can achieve. This paper describes the development of driver parking choice models (both during the journey and pre-trip) and the implementation of these models in the existing network traffic simulation model RGCONTRAM. Besides quantifying the effects of the PGI system on both the drivers seeking suitable parking spaces and the parking stock itself, this also enables quantification of the impact of parking choice on the wider network. Factors influencing PGI effectiveness are described and conclusions are drawn that illustrate the potential of PGI to induce the demand to spread more efficiently across the parking stock.

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies though experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed . In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.

An automated signalized junction control system that can learn strategies from a human expert has been developed. This system applies Machine Learning techniques based on Logistic Regression and Neural Networks to affect a classification of state space using evidence data generated when a human expert controls a simulated junction.The state space is constructed from a series of bids from agents, which monitor regions of the road network. This builds on earlier work, which has developed the High Bid auctioning agent system to control signalized junctions using localization probe data. For reference the performance of the Machine Learning signal control strategies are compared to that of High Bid and the MOVA system, which uses inductive loop detectors.Performance is evaluated using simulation experiments on two networks. One is an isolated T-junction and the other is a two junction network modelled on the High Road area of Southampton, UK. The experimental results indicate that Machine Learning junction control strategies, trained by a human expert can outperform High Bid and MOVA both in terms of minimizing average delay and maximizing equitability; where the variance of the distribution over journey times is taken as a quantitative measure of equitability. Further experimental tests indicate that the Machine Learning control strategies are robust to variation in the positioning accuracy of localization probes and to the fraction of vehicles equipped with probes.