Vehicle routing is traditionally based on Dijkstra or Dijkstra-like algorithms. These algorithms worked well for fossil fuel vehicles. The increase in pollution levels, government regulations, and pressure from environmental groups have caused an increase in electric vehicles (EVs) production and use. EVs are capable of regenerating energy which creates negative weights in search graphs that traditional algorithms are incapable of handling without some modifications. This study presents a model that characterises the energy consumption of an electric vehicle. Most passive and active factors are presented and applied in the formulation. The presented model is verified against 306 kilometres of driven data and proved to have 1.3% absolute error difference between the real vehicle's consumed energy versus the predicted energy consumption as generated by the model. The model is then used with a particle swarm optimisation algorithm to solve the single constraint optimisation problem of finding the most energy efficient route between 2 points on a map. Simulation and real-world test results demonstrate savings in the energy consumption of the electric vehicle. Results showed more than 9.2% reductions in the energy consumption of the electric vehicle when driven on the developed algorithms’ suggested routes rather than the ones generated by Google Maps and MapQuest.

Intersection control delay is one of the most important performance indicators for evaluating the traffic level of service and intersection capacities. In current traffic data detection infrastructure, control delay is not directly measurable. Although video-based detection approaches have been applied, their detection accuracy and reliability are constrained by application conditions and detection environments. Manual control delay data collection is labour-intensive, tedious, and time-consuming. High-resolution global positioning system (GPS) data provide an effective means of estimating control delays at intersections, but computationally intensive algorithms and hardware support are needed to handle a large network and impede their wide applications. In this study, a computationally cost-effective control delay estimation algorithm is developed based on low-resolution GPS-based transit bus trajectory data. Transit bus travelling behaviour is formulated to facilitate delay estimation. The effectiveness of the proposed algorithm is examined and verified by the field data and the results indicate that the proposed algorithm provides accurate and reliable control delay estimation at intersections under various conditions.

The smart e-bike monitoring system (SEMS) is a platform for the real-time acquisition of usage data from electrically-assisted bikes (also called pedelecs or e-bikes). It is autonomous (runs off the bike battery), replicable (open source and open hardware), scalable (different fleet sizes) and modular (sensors can be added), so it can be used for further research and development. The system monitors location (global positioning system), rider control data (level of assistance) and other custom sensor input in real time. The SEMS data feeds an online interface for data analysis, for riders to view their own data and for sharing on social media. The basic system can be replicated by other researchers and can be extended with modules to explore various issues in e-bike research. The source code and hardware design are publicly available, under the General Public License, for non-commercial use. SEMS was implemented on 30 bikes and collected data during 10 months of real-word trials in the UK. This study details the design and implementation of the hardware and software, discusses the system use and explores features for future design iterations. The SEMS turns singular e-bikes into a networked fleet and is an example of the internet of things in the cycling context.

This study presents a new optimal signal coordination scheme with an emphasis on emission reduction as a result of minimised passenger delay. By analysing the limitation of the algebraic method, a new algorithm using arrival–departure curves was proposed. A microscopic emission simulation platform based on VISSIM (a microscopic multi-modal traffic flow simulation model) and comprehensive modal emission model (CMEM) was established to estimate traffic emission reduction. A multi-objective genetic algorithm was used to find Pareto solutions that simultaneously reduce both passenger delay and vehicle emissions. Using the real-world traffic data collected in Changzhou city, an example of the signal coordination scheme was developed and its impact on emissions was simulated in the VISSIM-CMEM platform. The simulation results show that the proposed scheme achieved an average emission reduction of 28.8% on public transport vehicles and 18.7% on all vehicles over existing signal schemes.

Brake light detection of front cars has become a very important issue in safety of transport systems in recent years. As an adjunct component of automatic braking or warning systems, recognition and discrimination of the brake lights using vehicle-mounted cameras provides early warning to avoid rear-end collisions for the vehicles. Therefore in this paper a single camera-based segmentation method is introduced for detecting the brake lights in nighttime cruising and discriminating them from the other lights, such as tail lights and turn lights. Basically, a novel system is put forward for discriminating brake lights which is initialised with capturing the frames of front car having the tail lights on, with a mounted camera. Subsequent to acquisition, image enhancement is applied to frames for whitening the red corona and darkening the rest including the centre of the light sources. Region of interests are determined using the cumulative contrast differences as well as rear light positions with calculation of white and black pixel ratios in coronas. Yet, the tail lights have the approximately same ratio for all distances, ratios of the brake lights are significantly high, resulting in discrimination of brake lights from others, for the vehicles cruising in the dark.

The current study examines the possibility of automatically detecting distracted pedestrians on crosswalks using their gait parameters. The methodology utilises recent findings in health science concerning the relationship between walking gait behaviour and cognitive abilities. Walking speed and gait variability are shown to be affected by the complexity of tasks (e.g. texting) that are performed during walking. Experiments are performed on a video data set from Surrey, British Columbia. The analysis relies on automated video-based data collection using computer vision. A sensitivity analysis is carried out to assess the quality of the selected features in improving the accuracy of the classification. Classification results show that the proposed approach is promising with around 80% correct detection rate. This research can benefit applications in several transportation related fields such as pedestrian facility planning, pedestrian simulation models as well as road safety programmes and legislative studies.

Highway work zones pose a higher risk of crashes due to an increase in the complexity of driving task and driver information. An effective way to impart variable pertinent information to the drivers based on local conditions is the use of variable message signs (VMSs). This study focuses on the effectiveness of portable VMSs (PVMSs) in altering speeds using data obtained during a field deployment, as well as qualitative data from road users and construction workers collected using surveys. Speed, volume, and classification data were collected before and during the installation of the PVMS. A questionnaire was developed related to the PVMS performance, which was used to gauge the perception of drivers and construction workers for the purpose of qualitative analysis. The field data and the survey results were correlated to study the impact of PVMS in reducing the speed of vehicles. The survey results indicated that the drivers as well as the construction workers perceived PVMS to have a positive effect on safety and that PVMS provides reliable information. However, the quantitative analysis of vehicular speeds before and during the installation of PVMS revealed no statistically significant change in speeds.

Based on an empirical study of real field global positioning system data obtained from navigation devices in vehicles, the authors analyse fuel consumption of vehicles in city traffic. They show that synchronised flow patterns, revealed recently in real field oversaturated city traffic, exhibit considerable impact on fuel consumption. In particular, they have found out that fuel consumption in oversaturated city traffic can decrease considerably when oversaturated city traffic consists of synchronised flow patterns rather than consisting of moving queues of the classical traffic flow theory at traffic signals. Using empirical data from two different road sections in the city of Düsseldorf, Germany, the authors show that synchronised flow patterns and moving queues differ in their cumulated vehicle acceleration (a sum of positive speed differences along a vehicle trajectory) despite similar mean vehicle speeds. Fuel consumption in return is dependent on the cumulated vehicle acceleration. This latter dependency is obtained by means of a macroscopic consumption matrix based on empirical field trial consumption data and simulated speed and acceleration profiles. The authors sketch out the application of the study results to route guidance by demonstrating that the most energy-efficient route in a road network can differ from the fastest route.

With the development of both hardware and software technologies in camera and computer, automated visual inspection system is being used more and more in intelligent transportation system for its high efficiency. For the safety operation, it is necessary to perform fault inspection for train mechanical components. As one of the most widely used small mechanical components in freight trains, bogie block key (BBK) is used to keep wheel sets from separating out of bogies, and its fault is likely to cause terrible accidents. This study proposes a vision-based system to inspect the missing of BBK automatically. To ensure accurate and rapid fault inspection, a hierarchical detection framework consisting of fault area extraction and object detection is proposed. The purpose of fault area extraction is to divide image regions which contain the inspected component from the complex background. Subsequently, a component detector based on the sparse histograms of oriented gradients and support vector machine is proposed to verify the candidate image regions to check whether the BBK is missing or not. The experiments show that the proposed system realises the status inspection of BBK with high accuracy and high speed and can meet the need of actual applications.