Intelligent Traffic Management: A Review of Challenges, Solutions, and Future Perspectives
| Autor: | Shanta Ranga Swamy, Roopa Ravish |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
Computer science intelligent transport systems 05 social sciences General Engineering traffic management Computational intelligence 02 engineering and technology Congestion management K4011-4343 travel time prediction Transportation and communication Computer Science Applications congestion management traffic data collection Risk analysis (engineering) ComputerSystemsOrganization_MISCELLANEOUS computational intelligence 0502 economics and business intelligent traffic control 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Intelligent transportation system |
| Zdroj: | Transport and Telecommunication, Vol 22, Iss 2, Pp 163-182 (2021) |
| ISSN: | 1407-6179 |
| DOI: | 10.2478/ttj-2021-0013 |
| Popis: | Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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