Investigating the interaction between the parking choice and holiday travel behavior

Parking is one of the key links between the urban planning and transportation operation. However, most studies in this field focus on the parking behavior on workdays, and the holiday parking is seldom investigated. This study analyzes the interaction between the parking choice and travel behavior in the holidays. Data were collected at Fragrant Hills and Beijing Botanical Garden during the Qingming Festival (Tomb-sweeping Days) in 2013. The structural equation modelling was applied to examine the causal effects and quantitative relationships between the parking choice and holiday travel behavior and identify the main influencing factors based on the activity analysis. The results show that the parking choice has a close relationship with holiday travel behavior, which is more than an explanatory variable for the travel behavior. Moreover, the parking space availability, parking charge, and walking distance have significant effects on holiday parking choice. In addition, the personal attributes and household characteristics are significant influencing factors for the parking choice and holiday travel behavior

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connected-vehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations

Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment

We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from connected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various penetration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetration rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous prediction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data

A robustness approach to the distributed management of traffic intersections

[EN] Nowadays, the development of autonomous vehicles has emerged as an approach to considerably improve the traffic management in urban zones. Thanks to automation in vehicles as well as in other sectors, the probability of errors, typically due to repetitive tasks, has been drastically reduced. Therefore, technological aids in current driving systems are aimed to avoid or reduce human errors like imprudences or distractions. According to this, it is possible to tackle complex scenarios such as the automation of the vehicles traffic at intersections, as this is one of the points with the highest probability of accidents. In this sense, the coordination of autonomous vehicles at intersections is a trending topic. In the last few years, several approaches have been proposed using centralized solutions. However, centralized systems for traffic coordination have a limited fault-tolerance. This paper proposes a distributed coordination management system for intersections of autonomous vehicles through the employment of some well-defined rules to be followed by vehicles. To validate our proposal, we have developed different experiments in order to compare our proposal with other centralized approaches. Furthermore, we have incorporated the management of communication faults during the execution in our proposal. This improvement has also been tested in front of centralized or semi-centralized solutions. The introduction of failures in the communication process demonstrates the sensitivity of the system to possible disturbances, providing a satisfactory coordination of vehicles during the intersection. As final result, our proposal is kept with a suitable flow of autonomous vehicles still with a high communication fails rate.González, CL.; Zapotecatl, JL.; Gershenson, C.; Alberola Oltra, JM.; Julian Inglada, VJ. (2020). A robustness approach to the distributed management of traffic intersections. Journal of Ambient Intelligence and Humanized Computing. 11:4501-4512. https://doi.org/10.1007/s12652-019-01424-wS4501451211Ahn H, Colombo A, Del Vecchio D (2014) Supervisory control for intersection collision avoidance in the presence of uncontrolled vehicles. In: American control conference (ACC). IEEE, pp 67–873Ahn H et al (2016) Robust supervisors for intersection collision avoidance in the presence of uncontrolled vehicles (arXiv preprint). arXiv:1603.03916Bagloee SA et al (2016) Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J Mod Transp 24(4):284–303Bazzan ALC (2005) A distributed approach for coordination of traffic signal agents. Auton Agents Multi-Agent Syst 10(2):131–164Bazzan ALC, Klügl F (2014) A review on agent-based technology for traffic and transportation. Knowl Eng Rev 29(3):375–403Cools S-B, Gershenson C, D’Hooghe B (2013) Self-organizing traffic lights: a realistic simulation. In: Advances in applied self-organizing systems. Springer, Berlin, pp 45–55De Oliveira D, Bazzan ALC, Lesser V (2005) Using cooperative mediation to coordinate traffic lights: a case study. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems. ACM, pp 463–470Dresner K, Stone P (2005) Multiagent traffic management: an improved intersection control mechanism. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems. ACM, pp 471–477Dresner K, Stone P (2006) Traffic intersections of the future. In: Proceedings of the national conference on artificial intelligence, vol. 21. 2, Menlo Park. AAAI Press, Cambridge; MIT Press, London, p 1593Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intell Res 31:591–656Gershenson C (2004) Self-organizing traffic lights (arXiv preprint). arXiv:nlin/0411066Gershenson C (2007) Design and control of self-organizing systems. CopIt ArxivesGershenson C, Rosenblueth DA (2012) Self-organizing traffic lights at multiple-street intersections. Complexity 17(4):23–39Gonzalez CL et al (2018) Distributed management of traffic intersections. In: International symposium on ambient intelligence. Springer, Berlin, pp 56–64Gregor D et al (2016) A methodology for structured ontology construction applied to intelligent transportation systems. Comput Stand Interfaces 47:108–119Grünewald M, Rust C, Witkowski U (2006) Using mini robots for prototyping intersection management of vehicles. In: Proceedings of the 3rd international symposium on autonomous minirobots for research and edutainment (AMiRE 2005). Springer, pp 287–292Guo D et al (2003) A study on the framework of urban traffic control system. In: Proceedings of intelligent transportation systems, vol 1. IEEE, pp 842–846Ioannou P (2013) Automated highway systems. Springer Science and Business Media, New YorkKaplan J (2018) Digital Trends-Cars. https://www.digitaltrends.com/cars/every-company-developingself- driving-car-tech-ces-2018/. Accessed 22 Feb 2019Knight W (2013) Intelligent machines. https://www.technologyreview.com/s/520431/driverlesscars- are-further-away-than-you-think/. Accessed 22 Feb 2019Kosonen I (2003) Multi-agent fuzzy signal control based on real-time simulation. Transp Res Part C Emerg Technol 11(5):389–403Koźlak J et al (2008) Anti-crisis management of city traffic using agent-based approach. J Univ Comput Sci 14(14):2359–2380Lárraga ME, Alvarez-Icaza L (2010) Cellular automaton model for traffic flow based on safe driving policies and human reactions. In: Physica A: statistical mechanics and its applications 389.23, pp. 5425–5438. ISSN: 0378-4371Li P, Alvarez L, Horowitz R (1997) AHS safe control laws for platoon leaders. In: IEEE transactions on control systems technology 5.6, pp 614– 628. ISSN: 1063-6536Rasouli A, Tsotsos JK (2019) Autonomous vehicles that interact with pedestrians: a survey of theory and practice. In: IEEE transactions on intelligent transportation systemsRoozemond DA (2001) Using intelligent agents for pro-active, real-time urban intersection control. Eur J Oper Res 131(2):293–301Rothenbücher D et al (2016) Ghost driver: a field study investigating the interaction between pedestrians and driverless vehicles. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, pp 795–802Wang F-Y (2005) Agent-based control for networked traffic management systems. IEEE Intell Syst 20(5):92–96Wu J, Abbas-Turki A, El Moudni A (2012) Cooperative driving: an ant colony system for autonomous intersection management. Appl Intell 37(2):207–222Zangenehpour S, Miranda-Moreno LF, Saunier N (2015) Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: methodology and application. Transp Res Part C Emerg Technol 56:161–176Zapotecatl JL (2014) QtTrafficLights. https://github.com/Zapotecatl/Traffic-Light. Accessed 22 Feb 2019Zapotecatl JL, Rosenblueth DA, Gershenson C (2017) Deliberative self-organizing traffic lights with elementary cellular automata. In: Complexity 2017Zubillaga D et al (2014) Measuring the complexity of self-organizing traffic lights. Entropy 16(5):2384–240