In this paper, we propose a new data-driven traffic state estimation model that estimates traffic flow based on average speed data only. The model is devised to implement a cost-effective framework that aggregates heterogeneous sources of vehicles' GPS and speed measurements to infer traffic flow using a novel triplet system called Conditionally Gaussian Observed Markov Fuzzy Switching Systems (CGOMFSM). Unlike its hard counterpart, CGOMFSM allows for a transient and gradual representation of traffic state transition and hence improves the estimation performance using a tractable scheme. The potential of the proposed model is illustrated through an application to the problem of traffic incident detection, particularly sporadic traffic congestion caused by unexpected road conditions. The performance of the proposed model is assessed using real traffic datasets from England highways. A simulation of traffic in the city of Salalah in Oman was conducted to evaluate the efficacy of the CGOMFSM-based traffic estimation and incident detection schemes with different penetration rates.

Public and shared transportation are among the sectors that have been severely affected by the COVID-19 pandemic, as they were perceived to be risky environments for disease transmission. Given that the end of the pandemic is not certain and in order to anticipate future pandemics, attempts have been made to design public and shared mobility systems that are pandemic resilient, avoiding the social and economic burdens of disrupting transportation services. In this paper, we introduce a new ridesharing form based on a novel concept called social bubble vanpooling (SBV) which tries to provide a trade-off between minimizing the risk of exposure of riders to communicable diseases, minimizing the operational costs of ridesharing operators, and providing public health authorities with full contact-tracing capability in ridesharing-related cases, if needed. We propose a new clustering approach where riders are pooled into social bubbles composed of people who are spatio-temporally connected and have similar vulnerability levels with respect to a communicable diseases. We used individual agent-based simulation experiments based on a data sample collected from a real population of riders, and we compared the performance of the proposed SBV with trip-based and long committed ridesharing models. We found that (1) enforcing contact tracing and quarantine is more effective in controlling the spread of the disease when the bubble-based ridesharing scheme is adopted as a commuting mode and (2) it is possible to sustain transportation services without compromising the efforts to mitigate the spread of the pandemic. The proposed ridesharing model appears to be a viable solution when the mobility of individuals is subject to tight restrictions to stop the spread of a communicable airborne disease (such as COVID-19). The flexibility of the model allows maintaining transportation services with profitable operational costs while upholding the precautionary measures to fight the pandemic.

Efficient and accurate prediction of traffic state plays a major role in the implementation of effective intelligent transportation systems. Therefore, traffic forecasting has been attracting a significant interest over the last decades striving to efficiently achieve highest accuracy of reliable prediction algorithms. In this paper, we present a novel prediction algorithm based on the Conditionally Gaussian Observed Markov Fuzzy Switching Model (CGOMFSM). The proposed scheme relies on a triplet representation of traffic encompassing traffic flow, speed and a switch process in order to infer parameters that govern the dynamics of traffic. This work investigates the impact of explicit incorporation of fuzzy switching processes on the accuracy of traffic data prediction. It aims to overcome the shortcomings of several existing prediction schemes in which the crisp modeling of traffic dynamics is hindering the effectiveness of prediction. The experimental study shows that the proposed algorithm yields satisfactory results for a prediction horizon up to 60 minutes for data collected at regular 15-minutes intervals.

International audienceTraffic is one of the urban phenomena that have been attracting substantial interest in different scientific and industrial communities since many decades. Indeed, traffic congestions can have severe negative effects on people's safety, daily activities and quality of life, resulting into economical, environmental and health burden for both governments and organizations. Traffic monitoring has become a hot multidisciplinary research topic that aims to minimize traffic's negative effects by developing intelligent techniques for accurate traffic states' estimation, control and prediction. In this paper, we propose a novel algorithm for traffic state estimation from GPS data and using fuzzy switching linear models. The use of fuzzy switches allows the representation of intermediate traffic states, which provides more accurate traffic estimation compared to the traditional hard switching models, and consequently enables making better proactive and in-time decisions. The proposed algorithm has been tested on open traffic datasets collected in England, 2014. The results of the experiments are promising, with a maximum absolute relative error equal to 9.04%

With the increasing deployment of autonomous taxis in different cities around the world, recent studies have stressed the importance of developing new methods, models and tools for intuitive human–autonomous taxis interactions (HATIs). Street hailing is one example, where passengers would hail an autonomous taxi by simply waving a hand, exactly like they do for manned taxis. However, automated taxi street-hailing recognition has been explored to a very limited extent. In order to address this gap, in this paper, we propose a new method for the detection of taxi street hailing based on computer vision techniques. Our method is inspired by a quantitative study that we conducted with 50 experienced taxi drivers in the city of Tunis (Tunisia) in order to understand how they recognize street-hailing cases. Based on the interviews with taxi drivers, we distinguish between explicit and implicit street-hailing cases. Given a traffic scene, explicit street hailing is detected using three elements of visual information: the hailing gesture, the person’s relative position to the road and the person’s head orientation. Any person who is standing close to the road, looking towards the taxi and making a hailing gesture is automatically recognized as a taxi-hailing passenger. If some elements of the visual information are not detected, we use contextual information (such as space, time and weather) in order to evaluate the existence of implicit street-hailing cases. For example, a person who is standing on the roadside in the heat, looking towards the taxi but not waving his hand is still considered a potential passenger. Hence, the new method that we propose integrates both visual and contextual information in a computer-vision pipeline that we designed to detect taxi street-hailing cases from video streams collected by capturing devices mounted on moving taxis. We tested our pipeline using a dataset that we collected with a taxi on the roads of Tunis. Considering both explicit and implicit hailing scenarios, our method yields satisfactory results in relatively realistic settings, with an accuracy of 80%, a precision of 84% and a recall of 84%.