European in-vehicle emergency call system eCall aims to save lives and mitigate consequences of road accidents. eCall minimum set of data (MSD) which contains information on the accident vehicle is transmitted in the beginning of the call in the voice channel opened between in-vehicle system and public safety answering point. The main objectives of the study are to analyse the successfulness of eCall MSD transmission in Finnish fixed-line and GSM networks and to provide information on factors which contribute to the success rate of MSD transmission. The results of the Wilcoxon rank sum test indicated that the signal strength has no major impact on the success or failure of the MSD transmission when the mobile network signal strength measured by IVS is between −80 and −40 dBm. The analysed data included 12 failed MSD transmission attempts and 214 initiated MSDs in total. However, the test results also indicated that the MSD success rate can still be very different in different real-life physical mobile and fixed-line locations.

Variable speed limits (VSLs) and ramp metering (RM) have been proved to be effective alternatives to alleviate the traffic congestions occurred on freeways in many studies. However, no research has so far evaluated the efficiency of the above strategies on urban expressways in Chinese cities, where the early onset breakdown often occurs at the bottlenecks because of different driver behaviour from those reported in overseas. Hence, this study was intended to propose an integrated control strategy of VSL and RM to dynamically postpone the early onset breakdown of on-ramp bottlenecks based on its occurring probability. The proposed strategy is capable of simultaneously optimising mainline speed and RM rate based on the modified METANET model and the ALINEA model. Meanwhile, an online simulation platform that integrates VISSIM, Visual Basic and MATLAB programs was developed for the purpose of a close-to-reality evaluation. Empirical results on urban expressways in Shanghai show that the average travel time and the speed difference were significantly reduced by 8.6 and 41.4%, respectively; the pre-queue flow at the bottleneck was increased by 14.7%, which indicates that the early onset breakdown could be efficiently postponed or prevented by the use of the proposed control strategy.

This study presents a monovision-based system for on-road vehicle detection and computation of distance and relative speed in urban traffic. Many works have dealt with monovision vehicle detection, but only a few of them provide the distance to the vehicle which is essential for the control of an intelligent transportation system. The system proposed integrates a single camera reducing the monetary cost of stereovision and RADAR-based technologies. The algorithm is divided in three major stages. For vehicle detection, the authors use a combination of two features: the shadow underneath the vehicle and horizontal edges. They propose a new method for shadow thresholding based on the grey-scale histogram assessment of a region of interest on the road. In the second and third stages, the vehicle hypothesis verification and the distance are obtained by means of its number plate whose dimensions and shape are standardised in each country. The analysis of consecutive frames is employed to calculate the relative speed of the vehicle detected. Experimental results showed excellent performance in both vehicle and number plate detections and in the distance measurement, in terms of accuracy and robustness in complex traffic scenarios and under different lighting conditions.

This paper introduces a three layered approach built on robust microsimulation modelling to evaluate the benefits of integrated traffic management (ITM), when combining a MOVA (Microprocessor Optimised Vehicle Actuation) signal with Ramp metering (RM) and access management. The modelling uses additional controller parameters to simulate the coordination of two or more MOVA signal control systems over a relatively simple linkage, by providing an off-set. A case study has been chosen in a motorway environment by using actual data in Birmingham (UK) in response to development pressures. The results were audited through the Highways Agency procedures, were accepted and permission was granted. Signal technologies and advanced microsimulation modelling techniques have been used to manage access at the motorway interface to the local road network. Visual vehicle activated programming in VISSIM (VISVAP) programming techniques have been used to emulate the MOVA traffic signals, RM and the access management. The results show a significant operational benefit. The model predicted that the throughput of the mainline would increase by 9% and the overall average delay per vehicle would reduce by 37%, with the average speeds predicted to increase by 38% following the implementation of the ITM Strategy. A significant reduction in the total stopped time delay of 40.7% is also achieved thereby reducing the flow breakdowns.

In this study, the authors discuss the effective usage of technology to solve the problem of deciding on journey start times for recurrent traffic conditions. The developed algorithm guides the vehicles to travel on more reliable routes that are not easily prone to congestion or travel delays, ensures that the start time is as late as possible to avoid the traveller waiting too long at their destination and attempts to minimise the travel time. Experiments show that in order to be more certain of reaching their destination on time, a traveller has to leave early and correspondingly arrive early, resulting in a large waiting time. The application developed here asks the user to set this certainty factor as per the task in hand, and computes the best start time and route.

In this study a unified framework to carry out the simultaneous localisation and mapping of a mobile robot combining metric and topological techniques is presented. The robot moves in a real indoor environment and the algorithm makes use of the information provided by an omnidirectional camera mounted on the robot and its internal odometry. The hybrid approach consists in constructing simultaneously two maps of the environment, one metric and other topological with relationships between them which are updated in each step. The robot goes through the environment to build up a map while continuously captures images. To build the topological map the most relevant information from the scenes is extracted using a global appearance descriptor. A new node is added to the map when the appearance between two images is sufficiently different. Also, the authors check if there is a loop closure with a previous node. At the same time, a metrical map of the environment is computed. With this aim, the authors estimate the position of the robot when it captures a new image using a Monte–Carlo algorithm. The authors show how it is possible to obtain a reasonable performance both in time and accuracy in an indoor environment, when the involved parameters are properly tuned.