The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Evaluation of car traffic reduction potential in urban area, Paris and Lyon case-studies

The private car currently dominates travel in large metropolitan areas and its use is on the increase, in spite of the fact that public opinion is generally in favour of the development of public transport and political statements which reflect this opinion. Furthermore, the available projections and an analysis of the potential effect of conventional policies indicate that although such policies are able to exert some control, it is limited.Then, the question, that this research directed by INRETS will attempt to answer, is: could a major metropolitan area operate with a radically different transport system that is based principally on the use of modes other than the automobile? By "radically different", we mean a system in which use of the conventional automobile would be reduced in a non-marginal manner, by, say, between a third and a half of all private car vehicle-kilometres.This research does not attempt to justify a move towards a radically different system a lot as already been said on it. Instead, the project will perform different transport simulations and assess on based-rules the effect on the use of modes.Transport scenarios have been designed to incorporate a progressive improvement in public transport supply in the following respects: increase in speeds on the roads, increase in service frequencies during off-peak periods, creation of exclusive public transport lanes, reserving radial roads for public transport, extension of metro and regional express rail and reorganisation of bus routes in response to this. We have also devised and simulated a set of appropriate accompanying strategies that are intended to improve the effectiveness of public transport supply, for example policies to encourage the use of the bicycle or park and ride schemes. The methodology, developed by INRETS has been applied on Paris and Lyon region based on the last household travel survey conducted in each area. For each transport scenario, Paris and Lyon models are used to calculate public transport time for all trips whatever is the actual mode of transport. We then applied the procedure of mode transfer to assess the effect of each of these scenario on mode choice. The procedure is based on automatic rules. Trips, or more precisely round trips, are assigned to one or other of the alternative modes on the basis of elimination rules (no walking for distances over 2 kilometres, no cycling over 8 kilometres, no modal transfer if the purpose of the round trip is for escorting purposes...) and on the basis of constraints (individual travel-time budgets, the length of each trip and round trips, the existence of transport supply...). This system of rules and constraints constitutes the core of the modal transfer procedure.The paper will present both the methodology and results obtain from Paris and Lyon case studies.Urban transport ; Modal split ; modal split simulation method ; Transportation policy ; Car use reduction ; Paris (France) – Lyon (France)

The Development of a Common Investment Appraisal for Urban Transport Projects.

In December 1990 we were invited by Birmingham City Council and Centro to submit a proposal for an introductory study of the development of a common investment appraisal for urban transport projects. Many of the issues had arisen during the Birmingham Integrated Transport Study (BITS) in which we were involved, and in the subsequent assessment of light rail schemes of which we have considerable experience. In subsequent discussion, the objectives were identified as being:- (i) to identify, briefly, the weaknesses with existing appraisal techniques; (ii) to develop proposals for common methods for the social cost-benefit appraisal of both urban road and rail schemes which overcome these weaknesses; (iii) to develop complementary and consistent proposals for common methods of financial appraisal of such projects; (iv) to develop proposals for variants of the methods in (ii) and (iii) which are appropriate to schemes of differing complexity and cost; (v) to consider briefly methods of treating externalities, and performance against other public sector goals, which are consistent with those developed under (ii) to (iv) above; (vi) to recommend work to be done in the second phase of the study (beyond March 1991) on the provision of input to such evaluation methods from strategic and mode-specific models, and on the testing of the proposed evaluation methods. Such issues are particularly topical at present, and we have been able to draw, in our study, on experience of:- (i) evaluation methods developed for BITS and subsequent integrated transport studies (MVA) (ii) evaluation of individual light rail and heavy rail investment projects (ITS,MVA); (iii) the recommendations of AMA in "Changing Gear" (iv) advice to IPPR on appraisal methodology (ITS); (v) submissions to the House of Commons enquiry into "Roads for the Future" (ITS); (vi) advice to the National Audit Office (ITS) (vii) involvement in the SACTRA study of urban road appraisal (MVA, ITS

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated