Ben Waterson

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

This paper develops an innovative person-based control PB-ACAWB with complete flexible signal plans to minimize passenger delay of cars and buses in generalized 8-phases options junction. The algorithm is a scalable framework that can be implemented in both vehicular environments of all cars and mixture situations of buses and buses. The core part of PB-ACAWB uses a three-layered dynamic programming (DP) framework to efficiently figure out optimal signal timing plans to minimize passenger delay within a planning horizon. Additionally, a signal phase transition exploration mechanism is developed to explore all possible signal timing strategies for the next stage following the rules of the signal adjacent list. Departure time of cars and buses from all lanes are also predicted by proposed vehicle trajectory and car-following updating theories. A realistic case study in Birmingham, UK is also constructed in this paper to evaluate the performances of the proposed PB-AVAWB algorithm under a variety of scenarios. Overall, the results indicate that the proposed PB-ACAWB outperforms fixed time control TRANSYT, vehicle-based control VB-CVs and person-based control PB-CVs using CV data. The average delay of cars with 4 occupants and buses in PB-AVAWB is 25.3% - 38.4% and 54.3% - 64.5% less than those in VB-CVs with the objective of minimizing vehicle delay in all cases. The sensitivity analysis scenario shows that PB-AVAWB has stable performance for reducing average passenger delay when bus occupancy is higher or equal to 8 passenger/veh and planning horizon is 30s.

With the ability to accurately forecast road traffic conditions several hours, days and even months ahead of time, both travellers and network managers can take pro-active measures to minimise congestion, saving time, money and emissions. This study evaluates a previously developed random forest algorithm, RoadCast, which was designed to achieve this task. RoadCast incorporates contexts using machine learning to forecast more accurately contexts such as public holidays, sporting events and school term dates. This paper evaluates the potential of RoadCast as a traffic forecasting algorithm for use in Intelligent Transport System applications. Tests are undertaken using a number of different forecast horizons and varying amounts of training data, and an implementation procedure is recommended.

Wireless power transfer (WPT) offers a viable means of charging electric vehicles (EVs) 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 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 study reviews current traction battery technologies, conductive and inductive charging processes, influential parameters specific to the dynamic charging state as well as highlighting notable work 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 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 necessary standardisation, system architectures cannot be developed and implemented without fear of interoperability issues between 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.

Routing and scheduling software is part of the information and technology systems available to support the transport industry, and uses complex algorithms along with geographical representations of the road network to allow better planning of daily collection and delivery schedules. This paper reviews the evolution of routing and scheduling software, the algorithms used along with reported barriers to wider take-up and potential industry-driven improvements that could be made. A survey of transport companies in the U.K. was conducted in order to validate and prioritise the software capabilities that require the most development according to the new challenges that the industry is facing. Responses suggested that companies required improved route optimisation to tackle congestion based on time-dependent data and models, and greater accuracy in the representation of the road network. Not considering congestion leads to the underestimation of travel times and the production of inaccurate schedules. Literature shows that operational research techniques are available to solve problems that represent real-world conditions, but research into the relative merits of using time-dependent models needs to be undertaken. Data might be improved by cooperation between government and private sector.

Crowded trains are a feature of many railway networks and adversely affect both train passengers and rail operators. For passengers, the lack of space or inability to get a seat can lead to a lack of physical comfort, reduced productivity, and increased stress. Crowded trains can also lead to problems boarding and alighting that increase dwell times and make it harder for operators to provide a reliable service. Reducing levels of crowding is therefore desirable, but achieving this goal by increasing capacity is not always practical, and other measures must be considered. Some passengers have shown willingness to change their behavior to avoid crowding—for example, by waiting for a later train—and measures to encourage such behavioral changes more widely could be beneficial overall. Better information provision could be one such measure, and so a stated preference survey was undertaken on a commuter and airport service to investigate this issue further. It was found that the provision of information about crowding levels and seating availability on alternative trains would encourage some passengers to wait for a less-crowded train. Although the willingness of passengers to wait for a later train varied by both the purpose of the trip and the station of origin, the findings suggest that real-time information would improve the passenger experience and could form the basis of a revenue-neutral demand-management system. The implications for station design are particularly pertinent for countries such as the United States, where significant investment in new passenger rail systems is expected.

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’ search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable time.\ud \u

This paper uses automatically-recorded vehicle number plate data from a network of 22 cameras in Dorset, UK, to investigate the extent to which regular trip making can be determined using the regularity of individual vehicle arrival times across the same sites and time intervals over extended periods of several months and illustrates how a cohort of recognised regular vehicles may provide indicative evidence of traffic delays. Regularity was defined based on minimum numbers of observations over a given period and with specified maximum values of standard deviation in arrival time, with sensitivity to different values being tested. It was found that around one-fifth of all vehicles were regular during the morning peak where the definition required at least 30 observations out of 210 working days and with a standard deviation in arrival time of no more than ten minutes; significantly fewer vehicles were found to be regular in the afternoon peak. The turnover, or churn, of regular vehicles was found to be considerable, with only one-tenth of defined regular vehicles being continuously regular throughout the period and with identified pools of regular drivers halving in size every three months, as vehicles ceased to be regular and where the pool was not updated. This suggests that any database of regular drivers should be updated at least quarterly to ensure that new regular vehicles are included and that old ones are discarded. These findings may have inferences for traffic information systems tailored for different driver groups according to assumed levels of network knowledge.