Fuel economy is closely related to driving style, traffic situation, and urban geomorphic environment. In this study, the authors propose to compare fuel consumption based on normalised driving behaviour. A personalised driver model is established for each driver by using the locally designed neural network and the real-world vehicle test data. Driving behaviour is normalised by employing the personalised model to conduct the speed-following task as defined by standard driving cycle test. Based on the normalised driving behaviour, an aggressiveness index is used to quantitatively evaluate the driving style, and a fuel index is proposed to estimate the fuel consumption. A case study is conducted on the fuel consumption comparison in four major cities in China, namely, Beijing, Shanghai, Chongqing, and Nanjing. Computational results verify the effectiveness of the proposed scheme.

Efficient driving has been positioned as the most popular alternative to reduce air pollution and obtain fuel savings. However, efficient driving requires a continuous learning process in order to prevent users reverting to their original habits. To facilitate the learning process, on-board tutoring systems have appeared. In this study, the authors analyse in detail the impact of two types of such eco-feedback devices on driver's behaviour. The evaluated tutoring systems include information related to the optimal engaged gear, but also related to other safety and comfort parameters. Their analysis is based on one of the largest and heterogeneous population groups of non-professional drivers who have participated in experiments with feedback devices specifically designed to achieve more efficient driving. A total number of 158 volunteers participated in the experiments covering periods of time between 3 and 11 months and using the feedback devices during their daily routine. Results show that, in general, users evolve positively following the eco-driving recommendations throughout the duration of the experiments. In addition, there are significant differences in the use of the tutoring system depending on the type of route, the time of day and other factors such as age or gender.

Through vehicle-to-everything traffic information propagation often causes data outliers, due to data delay, data loss, inaccurate data and inconsistent data. Traffic data (TD) with outliers may incorrectly describe traffic conditions and decline the reliability and stability of transportation cyber physical system. This study develops some research approaches to detect spatiotemporal (ST) data outliers for the development of transportation systems. These research approaches include the theorisation of ST traffic outliers, the creation of an innovative firefly algorithm (IFA), the discussion of TD synchronisation methods and the development of the FA-based ST outlier detection mechanism (IFA-STODM). The experimental results show that the proposed IFA-STODM is an effective and efficient method for the detection of ST TD outliers.

An increased number of sensors used in traffic networks to obtain more information will lead to higher costs. This study proposes a minimum investment model for optimal traffic network sensor layout, which considers the spatial distribution characteristics of traffic information, homogeneity of various link locations, and total project costs. The model divides a road network into link sections and network nodes and determines the key points. Through segmentation, the model simplifies the network optimisation problem into a multi-section optimisation problem, and the model is tested and validated in a field test for a typical road network consisting of an expressway and several arterial roads in Beijing. It is shown that the proposed model effectively solves the network sensor location problem for traffic information acquisition. While comprehensive and reliable traffic information is obtained at any given location in the road network to meet the practical engineering requirements, this model produces sensor layout strategies with minimum cost, and provides guidance for engineering applications in the field. Finally, by analysing the sensitivity of model parameters, some recommended sensor layout strategies are proposed to reduce the investment cost and provide a decision-making basis for traffic information acquisition implementation and fine traffic management.

The purpose of this study is to present a new method for lane-based traffic demand estimation using travel times from video-imaging detectors. The method overcomes the following two shortcomings of loop-detector-based algorithms: the fact that the actual demand is unknown when detectors are located upstream from the stop lines within a short distance; and the difficulty in calculating the ratio between streams in different lane groups if detectors are located at the upper reaches of the links. First, the authors analyse a variety of travel time patterns and introduce the concept of a virtual cycle that satisfies the criteria that all vehicles entering into a link in one virtual cycle have just departed from a downstream stop line within a single signal cycle. Next, the authors improve the travel time reduction rate model for queued vehicles in each cycle, and enhance the algorithms to estimate the lane-based traffic demand under different conditions. Finally, all parameters are calibrated and the new models are evaluated. The results show that: the maximum, minimum and average deviations over 12 cycles are 38.50, 0.02 and 16.19%, respectively. The findings in this study have potential applicability for use in traffic control systems, especially where oversaturated conditions are present.

Driver awareness of current winter road conditions (RCs) is known to affect the frequency of accidents due to sudden changes in these conditions. For example, partially icy roads that appear during autumn in northern areas typically result in collisions and ditch runs unless the drivers are generally aware of the situation. Availing motorists who drive under winter RCs of enhanced information is therefore highly desirable to increase their awareness of hazardous driving conditions. Such conditions need to be predicted ahead of time and presented to drivers before they attempt slippery road sections. Moreover, the identification of slippery RCs should quickly trigger targeted road maintenance to reduce the risk of accidents. This study presents a scalable and reusable collaborative intelligent transport system, herein referred to as an RC information system (RCIS). RCIS provides accurate RC predictions and forecasts based on RC measurements, road weather observations, and short-term weather forecasts. The prediction methods in the context of the distributed RCIS have been tested using a prototype implementation. These tests confirmed that these inputs could be combined into useful and accurate information about winter RCs that can be adapted for different types of users.

Acquiring the relative displacement of adjacent vehicles when designing a distributed controller for a heavy-haul train is a critical issue. As heavy-haul trains run in complex, varying environments, the relative displacement of adjacent vehicles may not be measured accurately. To address this issue, this study proposes a distributed cooperative observer to estimate the relative displacement among train vehicles. First, a mass-point elastic-coupled dynamic model is constructed to capture the relative displacement of a heavy-haul train, and the complex model is further decomposed into a series of reduced double integrators. Then, a distributed cooperative dynamic observer is designed for each reduced double integrator. State information is shared via communication among neighbouring observers to accurately estimate the relative displacements of the vehicles under system noise. The convergence of the cooperative observer error is analysed using a Riccati equation. Finally, the performance of the closed-loop heavy-haul train control system combined with the cooperative observer is rigorously evaluated using real parameter settings of Shuohuang Railway in China.

Precise and robust distance measurement is one of the most important requirements for driving assistance systems and automated driving systems. In this study, the authors propose a new method for providing accurate distance measurements through frequency-domain analysis based on a stereo camera by exploiting key information obtained from the analysis of captured images. Moreover, the proposed method was extensively tested and evaluated on a real urban road, highway and tunnel. Based on these results, the authors show that the proposed method provides more precise distance information in real time compared with conventional algorithms. By applying the authors' methodology to measure the distances of various objects, it can be demonstrated that their algorithm offers an improvement of up to 10%.