The proliferation of technologies embedded in connected and autonomous vehicles (CAVs) increases the potential of cyber-attacks. The communication systems between vehicles and infrastructure present ...remote attack access for malicious hackers to exploit system vulnerabilities. Increased connectivity combined with autonomous driving functions pose a considerable threat to the vast socioeconomic benefits promised by CAVs. However, the absence of historical information on cyber-attacks mean that traditional risk assessment methods are rendered ineffective. This paper proposes a proactive CAV cyber-risk classification model which overcomes this issue by incorporating known software vulnerabilities contained within the US National Vulnerability Database into model building and testing phases. This method uses a Bayesian Network (BN) model, premised on the variables and causal relationships derived from the Common Vulnerability Scoring Scheme (CVSS), to represent the probabilistic structure and parameterisation of CAV cyber-risk. The resulting BN model is validated with an out-of-sample test demonstrating nearly 100% prediction accuracy of the quantitative risk score and qualitative risk level. The model is then applied to the use-case of GPS systems of a CAV with and without cryptographic authentication. In the use case, we demonstrate how the model can be used to predict the effect of risk reduction measures.
► New approach for short-term road traffic prediction. ► Effective on urban road networks, both expressway and arterial roads. ► Scalable from medium-sized to large urban or inter-urban networks. ► ...Highly accurate up to one hour in advance.
Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1
h in advance.
•A lane-changing advisory control was proposed using C-ITS.•Hyper-heuristic method was used to optimize lane-changing advisory proportions.•Effects of lane-changing advisory with different connected ...vehicles were analyzed.
Reduction in the number of lanes (lane drop) is common on motorways due to road design, incidents, or road maintenance, and it can be an active bottleneck if the traffic demand is high. If congestion occurs, the lane drop capacity will decrease 10–20%. In order to avoid capacity drop, this study analysed the reason and proposed lane-changing advisory control on the merge lane to distribute lane-changing using Cooperative Intelligent Transport Systems (C-ITS) technology. Further, this study used hyper-heuristic optimisation to obtain the lane-changing advisory proportion of each segment upstream of lane drop. Conditions of different traffic demands were analysed using the microscopic traffic simulation software AIMSUN and its Application Program Interface (API) function. Results indicated that the proposed lane-changing advisory strategy could reduce traffic congestion and obviously improve traffic efficiency. This study also analysed the effects with different proportions of connected vehicles and found that if the connected vehicle ratio is less than 20%, the lane-changing advisory has little impact on the lane drop performance, and that if the penetration rate is more than 50%, the gain in performance is marginal.
The scoping review reported by this article aimed to analyze and synthesize state-of-the-art studies focused on the integration of cyber resilience in the implementation and deployment of intelligent ...transport systems, namely in terms of (i) cyber resilience functions being considered, (ii) relative importance of the different transport types, and (iii) how the proposed solutions are being validated. An electronic search was conducted, and 23 studies were included in this review after the selection process. According to the findings, cyber resilience represents a gap of the current research related to intelligent transport systems and, therefore, additional efforts are required to guarantee that they are resilient to cyber-attacks and other adverse events.
•Designing one-of-a-kind experiment to monitor urban congestion with a swarm of drones.•Creating the most complete urban multimodal dataset, nicknamed pNEUMA, to study congestion.•Investigating ...traffic phenomena at different scales of modeling.•Developing an open science initiative with almost half a million trajectories for transportation-oriented research.
The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.
•A cooperative variable speed limit system is proposed.•Infrastructure to vehicle communication and autonomous vehicle control are used.•Potential benefits are studied by the use of microscopic ...traffic simulation.•The system contributes to flow harmonization and reduced exhaust emissions.•There are no adverse effects of the system on the mean speed on the road.
Variable speed limit systems where variable message signs are used to show speed limits adjusted to the prevailing road or traffic conditions are installed on motorways in many countries. The objectives of variable speed limit system installations are often to decrease the number of accidents and to increase traffic efficiency. Currently, there is an interest in exploring the potential of cooperative intelligent transport systems including communication between vehicles and/or vehicles and the infrastructure. In this paper, we study the potential benefits of introducing infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits in variable speed limit systems. We do this by proposing a cooperative variable speed limit system as an extension of an existing variable speed limit system. In the proposed system, communication between the infrastructure and the vehicles is used to transmit variable speed limits to upstream vehicles before the variable message signs become visible to the drivers. The system is evaluated by the means of microscopic traffic simulation. Traffic efficiency and environmental effects are considered in the analysis. The results of the study show benefits of the infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits for variable speed limit systems in the form of lower acceleration rates and thereby harmonized traffic flow and reduced exhaust emissions.
•A customized end2end spatio-temporal deep learning model for multistep prediction.•Integrate Seq2Seq, GCN and attention mechanism along with a new training method.•Two real-world datasets to ...evaluate algorithm performances and acquire insights.
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynamics and spatial correlations in multistep traffic-condition prediction, we propose a novel deep learning framework named attention graph convolutional sequence-to-sequence model (AGC-Seq2Seq). In the proposed deep learning framework, spatial and temporal dependencies are modeled through the Seq2Seq model and graph convolution network separately, and the attention mechanism along with a newly designed training method based on the Seq2Seq architecture is proposed to overcome the difficulty in multistep prediction and further capture the temporal heterogeneity of traffic pattern. We conduct numerical tests to compare AGC-Seq2Seq with other benchmark models using two real-world datasets. The results indicate that our model yields the best prediction performance in terms of various prediction error measures. Furthermore, the variations of spatio-temporal correlations of traffic conditions under different perdition steps and road segments are revealed.
Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural ...network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.
•Indicators of smart transport should reflect smart capabilities in transport sub-systems.•A comprehensive framework for benchmarking smart transport cities is proposed.•The framework is illustrated ...by benchmarking 26 large cities across the world.•London, Seattle and Sydney ranked high in the illustrative example.•The framework allows comparative studies among cities in different transport systems.
Besides responding to challenges of rapid urbanization and growing traffic congestion, the development of smart transport systems has attracted much attention in recent times. Many promising initiatives have emerged over the years. Despite these initiatives, there is still a lack of understanding about an appropriate definition of smart transport system. As such, it is challenging to identify the appropriate indicators of ‘smartness’. This paper proposes a comprehensive and practical framework to benchmark cities according to the smartness in their transportation systems. The proposed methodology was illustrated using a set of data collected from 26 cities across the world through web search and contacting relevant transport authorities and agencies. Results showed that London, Seattle and Sydney were among the world’s top smart transport cities. In particular, Seattle and Paris ranked high in smart private transport services while London and Singapore scored high on public transport services. London also appeared to be the smartest in terms of emergency transport services. The key value of the proposed innovative framework lies in a comparative analysis among cities, facilitating city-to-city learning.
This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective ...stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.
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