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In diesem Kapitel werden die Auswirkungen von Fahrzeugpannen auf die Verkehrsleistung anhand von V2V-Simulationen auf einem Abschnitt der Autobahn M1 in Großbritannien untersucht. Es untersucht das Potenzial der Technologie für vernetzte Fahrzeuge (Connected Vehicle, CV), um Verkehrsvorfälle zu erkennen und abzumildern, die Sicherheit zu erhöhen und Verzögerungen zu verringern. Die Studie verwendet ein ausgeklügeltes Verkehrsmikrosimulationsmodell in VISSIM, das mit Daten aus der realen Welt kalibriert wurde, um verschiedene Szenarien mit unterschiedlichen CV-Penetrationsraten zu bewerten. Zu den wichtigsten Ergebnissen gehört die deutliche Verringerung von Fahrzeugverzögerungen und Verkehrskonflikten, da die Verbreitung von Lebensläufen zunimmt, wobei das Szenario einer 100% igen Lebensdauer die wesentlichsten Verbesserungen aufweist. Die Forschung unterstreicht auch die Bedeutung einer zuverlässigen V2V-Kommunikation, um diese Vorteile zu erzielen. Das Kapitel kommt zu dem Schluss, dass die CV-Technologie, insbesondere die V2V-Kommunikation, vielversprechende Lösungen zur Verbesserung des Verkehrsunfallmanagements und der Sicherheit auf Autobahnen bietet.
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Abstract
Road traffic congestion has adverse effects on commuter safety and transport network efficiency, apart from its environmental consequences. To address this issue, Traffic Incident Detection (TID) models have been developed, leveraging advanced connectivity technologies. However, ensuring the alignment and effective operation of these technologies within existing systems and contexts is critical. This research aims to create an incident detection algorithm supported by Vehicle-to-Vehicle (V2V) technologies, alerting road users approaching incident zones. The algorithm’s effectiveness was assessed through metrics like vehicle delays, travel time, and macroscopic fundamental diagrams (MFDs). Real-time traffic conditions were simulated using VISSIM, employing data from Inductive Loop Detectors (ILDs) and ground truth data from an instrumented vehicle on a UK motorway section. Results reveal varying impacts on delays and overall traffic based on V2V adoption rates. The presence of Connected Vehicles (CVs) ensures efficient traffic flow. These insights benefit network operators, enabling prompt identification and communication of traffic incidents to drivers, roadside infrastructure, and traffic control centers, ultimately aiming to mitigate traffic and safety impacts.
1 Introduction
Current transport systems need to be more economically efficient, environmentally benign, and safe. Recent advancements in artificial intelligence, connected capability, and vehicle technology have brought about the introduction of CVs closer to reality. CVs can learn, adapt, make decisions, and act independently of human control and are, therefore, envisaged to impact safety, mobility, and society profoundly collisions [1]. For instance, once a traffic incident occurs on a network, CV technology has the potential to detect the incident quickly and mitigate its impact on traffic efficiency and safety.
While studies have explored on-demand mobility, MaaS, and CVs for detecting unusual traffic dynamics and improving road safety [2‐9], limited research addresses mitigating traffic incident consequences with CV technology. CVs offer solutions through V2V tech. While V2V typically focuses on traffic info sharing [10] and route guidance [11], its potential in incident scenarios remains underexplored. It can leverage its route coordination system [11] and online environment [12] to manage incidents, relying on link travel time prediction. A robust wireless communication network forms the core of connected transport systems [13]. Reliable V2V communication is crucial for CV tech to share traffic data, enhance awareness, reduce congestion, and ensure safety.
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Detecting traffic incidents on a high-speed road network is a systematic endeavor to locate, react to, and eliminate traffic incidents and re-establish safe traffic capacity within satisfactory timeframes [14]. The research gap of this work is identified as there is a need to quantify the impact of CVs in case of an incident through the formulation of a traffic simulation framework capable of modelling connected vehicles along with traditional vehicles in a variety of pre-defined scenarios, with regards to maximising the traffic performance and road safety on highways.
CV technology enables vehicle communication and navigation enhancements, offering valuable information for informed, safer decisions [15, 16]. CVs create data-rich environments, enhancing road safety, reducing congestion, and improving eco-friendliness [17]. Growing demand for “always connected” vehicles drives innovation for safer, more connected software [17, 18]. Integrating connectivity into CVs requires reliable technologies for deployment, reliability, and functionality in ITS [19]. These technologies ensure seamless data exchange between smartphones and in-vehicle networks [20, 21]. Current wireless CV communication tech includes DSRC, Wi-Fi, and V2V communication [17, 22]. Achieving reliable, secure V2V connectivity demands feasible solutions for integration with CV-installed V2V products [22].
V2V technology in CVs aims for earlier incident detection through smart algorithms [23]. These models offer real-time traffic info before and during incidents, reducing secondary crashes, incident duration, and fuel consumption [14, 24]. Effective use of CV tech like V2V relies on data collection (delays, driver behavior, travel time) and informing drivers to take proactive actions like slowing down and changing lanes [25, 26]. Previous methods like Advisory Radio and Variable Message Signs (VMS) provide traffic info but have limitations [27, 28]. Intelligent options like CVs, relying on wireless communication for V2V interactions, offer more flexible solutions [27, 29].
Although many approaches have been reported, the performance of incident detection models using CV technologies highly relies on their deployment context. Although such models have started to be widely explored and implemented worldwide, no reasonable attempts have been made in the United Kingdom’s specific context. In addition, some studies have examined models based on CV technology focusing on recurrent congestion issues [30, 31] and do not address nonrecurrent congestion problems for traffic incident detection.
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In this paper, to preach the abovementioned weakness, a smart Traffic Incident Detection algorithm by leveraging the use of V2V communication protocol has been used. This approach is being tested by using various measures of effectiveness. The incident detection and mitigation model has been used in a section of the M1 Motorway in the UK.
2 Methodology
The purpose is swift CV-based traffic incident detection to reduce delays and enhance safety. Prior studies suggest a 50% incident duration reduction could cut total delay by 75% [32]. As real-world CV incident data are unavailable, a simulation approach was chosen. To develop the micro-simulation incident model, three data types were necessary: (1) traffic data (volume, composition, speed, travel time); (2) geometric data (lane count/width, road type); and (3) incident data (type, location, time, duration). Geometric data came from Google Earth and National Highways. National Highways provided traffic and incident data from 2016 to 2019. Inductive Loop Detectors (ILDs) data, available on WebTRIS [33], supplemented the datasets. ILDs collect, process, and transmit dynamic and static traffic data at 500 m intervals.
2.1 Incident Detection Algorithm Using V2V
The overall methodology consists of five modules which are briefly discussed below:
1.
Understanding existing traffic incidents and their characteristics (e.g., types, location, duration, trends)
2.
Developing a traffic microsimulation model in VISSIM (including its calibration)
3.
Modifying V2V communication protocol and simulating incidents
4.
Creating representative scenarios for incident occurrences based on Item 1
5.
Measuring the effectiveness of V2V relative to the base-case scenario
2.2 Understanding Traffic Incidents and Their Characteristics
In on-road settings, incident detection challenges include delayed detection and frequent alarm rate [34]. CVs and V2V tech help by instantly notifying following vehicles about incidents ahead, ensuring quicker detection and allowing road users and control centers to react promptly.
2.3 Traffic Microsimulation Model - Building Baseline Model
A 100 km stretch of the UK’s M1 motorway, managed by National Highways, serves as the testbed. It spans from Junction 23 (west of Loughborough) to Junction 26 (northwest of Nottingham). The area includes the main motorway, six roundabouts, and a parking/rest area, all meticulously designed. Figure 1 represents the whole network as a building in VISSIM.
2.4 Calibration and Validation of the Simulation Model
Calibration is an iterative optimization process aiming to minimize the difference between real-world and simulated data. It starts with default parameters, simulating real-world conditions. An initial number of simulation runs are executed to receive a dataset used for comparison with the real world. When simulated data matches real-world data adequately, the process ends. Parameters are then validated similarly. If the error remains acceptable, additional validation follows, including Geoffrey E. Havers (GEH) statistics and Mann Whitney U test. All goodness-of-fit measures met acceptable standards [35, 36].
Fig. 1.
(a) Whole simulated motorway network as depicted in VISSIM. (b) The incident simulation in the VISSIM interface
Figure 1a and b are screenshots reproduced with permission from PTV Group, copyright PTV Group, [20, 24].
2.5 Simulating Incidents
When an incident needs to be simulated, the method following includes simulating a parking lot that allows the vehicles involved in the incident to stay in the blocked lane during the incident duration, for following vehicles to use the allowable lanes. In this research, for the incident to be simulated, the parking lot was placed on the slow lane in the middle of J24 between an exit and an entry ramp. The parking lot is assigned to the vehicle that caused the incident, as illustrated in Fig. 3. The partial route controls all traffic over created connectors during the incident. Fig. 1b shows the incident simulation in the VISSIM interface.
2.6 V2V Communication in VISSIM
To differentiate between the V2V classes of vehicles, several colours were used. In this regard, white (no V2V equipment), yellow (inactive V2V message), and green (received V2V message). VISSIM’s user-defined attribute specified these classes. Figure 1 shows car identification, incident simulation, and the attribute. The message is being transferred to a range determined, and the time distribution of transmitting the message is the headway of the vehicles. When approaching the incident location, the vehicles are forced to change lanes using the allowable lanes. When the vehicles with V2V pass the incident location, they stop receiving the message and turn from green to yellow again.
To simulate V2V vehicles, an event-based Python 3.7 script file was used. It automatically ran several times before the simulation started and when the incident began. The External Driver Model API COM enhanced incident detection with varying CV penetration rates.
2.7 Developing Representative Scenarios
Multiple scenarios were developed and examined for different CVs’ MPR. The incident happened at 16:30 with an incident duration of one hour out of a five-hour simulation. The scenarios were created:
i.
Baseline Scenario: 100% normal vehicles, 0% CVs, HGVs
ii.
25% CVs Scenario: 75% normal vehicles, 25% CVs, HGVs
iii.
50% CVs Scenario: 50% normal vehicles, 50% CVs, HGVs
iv.
75% CVs Scenario: 25% normal vehicles, 75% CVs, HGVs
v.
100% CVs Scenario: 0% normal vehicles, 100% CVs, HGVs
2.8 Measuring Effectiveness
The effectiveness of the incident detection model can be tested by comparing the macroscopic fundamental diagrams (MFD), the average vehicle delay, the queue length, and the traffic conflicts detected by the Surrogate Safety Assessment Model (SSAM). Ten simulation runs were performed with different random seeds for each market penetration scenario and an incident occurring in the same location. Each simulation run lasted 10,800 simulation seconds with 1,800 simulation seconds warm-up period to allow the motorway segment to be fully occupied.
3 Results
The V2V algorithm was evaluated by comparing MFD, delays, and conflicts between the baseline and other scenarios. Evaluations took place at 16:30 in the simulation. Most scenarios didn’t reach the network’s full capacity, indicating low congestion when one lane out of four was closed for a one-hour incident. It’s worth noting that SRN incident durations vary from 30 min to two hours. Despite minimal traffic congestion at the incident site, increasing the MPR of CVs led to a significant reduction in conflicts. Figure 2 shows MFD for different scenarios, while Fig. 3 displays the average vehicle delay (in seconds) for each MPR scenario (a), queue length results for each MPR scenario (b), and percentage reduction in traffic conflicts with the baseline scenario per MPR of CVs (c).
The MFD for the volume and speed were created using the link segment data from the simulation results. Figure 2 compares the volume and the speed-based MFDs obtained from the granular approximations to check the capacity of the network in a section of the network approximately 2 km before the incident location, from junction 23A to junction 24. Even though different spatial and temporal demand patterns were simulated, including different MPR of CVs, the average volume and speed are consistent and closely predicted for all scenarios. Both diagrams depict real-world data used for the simulated network.
For MFD flow/density the maximum flow rate represents the highest traffic flow rate the highway can handle. In the case of the baseline scenario, the capacity reached 4,550 vehicles per hour, while scenarios with 75% and 100% MPR of CVs appeared to have reached more at 4700 vehicles per hour. In contrast, 25% and 50% MPR of CVs reached a slightly lower capacity, just under 4,500 vehicles/hour, even below the baseline.
In MFD speed/density, speed remains constant at capacity levels, indicating smooth, congestion-free traffic flow even at high densities. The road network handles demand without congestion or significant delays. All vehicles in all scenarios maintained an appropriate 110 km/h speed limit in free flow. As average density increases, speed decreases until reaching jam density, where it’s lowest. This pattern is observed in the baseline and 50% and 75% MPR CV scenarios, with a minimum speed of 85 km/h and moderate congestion.
Fig. 3.
Average delay per vehicle (in seconds) per MPR scenario – a; Average queue length results per MPR scenario – b; Percentage reduction in traffic conflicts per MPR of CVs with the baseline scenario – c.
The effect of the breakdown has also been examined through the average delay of a vehicle in seconds and the average queue length created due to the breakdown per MPR scenario. In Fig. 3a the baseline case with no connectivity experiences the worst travel conditions, resulting in higher delays compared to the 100% MPR CV scenario, which exhibits improved travel conditions during the 1-h breakdown. The 25% and 50% MPR CV scenarios show increased delays per vehicle, while the 75% and 100% MPR CV scenarios have better traffic profiles than the baseline. Beyond a 50% MPR, increasing MPR may lead to higher average delays due to potential negative effects. Conversely, MPR levels above 75% result in reduced delays compared to the baseline, indicating that the benefits of CV communication and coordination outweigh the potential drawbacks.
Additionally, at high MPR levels, fewer conflicts arise between connected and non-connected vehicles, reducing accident risks and unexpected delays. The lowest delays were seen at 100% MPR of CVs. Similar trends apply to average queue length during the 1-h lane closure (Error! Reference source not found.), mirroring vehicle delay patterns. Once more, the shortest stop delays occurred at 100% MPR of CVs.
In Error! Reference source not found., the percentage decrease in conflict types compared to the baseline scenario is depicted. SSAM identified three conflict types: rear-end conflicts, lane-change conflicts, and crossing conflicts. As expected, Error! Reference source not found. illustrates that increased adoption of CVs leads to fewer conflicts during traffic incidents, resulting in a positive reduction in total conflicts, ranging from 12% to 25% based on the MPR of CVs. This signifies an overall enhancement in safety, reducing potentially perilous situations during incidents. However, the 25% MPR CV scenario exhibits a negative decrease in lane-change conflicts compared to the baseline, possibly due to behavioral shifts and communication delays. In summary, the baseline scenario had the highest total conflicts, whereas the 25% MPR CV scenario had the lowest, with the other scenarios still delivering positive safety impacts, underscoring the efficacy of V2V technologies.
4 Discussion and Conclusion
This research aimed to test V2V technology’s impact on reducing traffic incident consequences. It involved real-time incident detection via V2V communication and simulation to study various MPRs of CVs regarding capacity, delays, and conflicts. Simulating CV incidents is a challenging aspect with limited literature coverage.
Simulations showed promising results for mitigating traffic and safety impacts with CV technology. MFD shapes aligned with theoretical relationships, confirming accurate calibration. Outcomes vary with CV MPR and traffic volume. MPR above 75% reduced delays and queues in non-congested areas, with fewer conflicts in high MPR. 100% MPR was the safest scenario, followed by 75%, while no connectivity was the least safe.
This paper demonstrates the effectiveness of V2V incident detection with various CV penetration rates. CVs interact via V2V, with stopped vehicles sending notifications. Following vehicles adjust speed and behavior based on received messages.
VISSIM 2021 validated the analytical method on mixed traffic corridors with varied CV MPRs. Future work should consider HGV connectivity and high-congestion scenarios.
This framework benefits traffic management, car manufacturers, and traffic authorities, aiding safety improvements, CV settings, and traffic demand management. It enables real-time incident detection and communication for reduced impacts and collision prevention.
In summary, this interdisciplinary research integrated computer programming, traffic, simulation, and V2V communication to create an incident detection framework using VISSIM and API COM Interface. Future extensions could include network-level CV simulations in congested environments and diverse scenarios.
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Gupta, A., Suman, S., Bhupendra, S.: Framework for development of advanced traveler information system : a case study for Chandigarh city. Urban Transp. J. 14, pp. 75–83 (2015)
2.
Quddus, P.M., Imprialou, M., Kirk, A.: School of civil and building engineering transport studies group first year report: of connected and autonomous vehicles Lampros Alexandros Papadoulis supervisors (2016)
3.
Katrakazas, C., Quddus, M., Chen, W.H., Deka, L.: Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp. Res. Part C Emerg. Technol. 60, 416–442 (2015)CrossRef
4.
Rahman, M.S., Abdel-Aty, M.: Longitudinal safety evaluation of connected vehicles’ platooning on expressways. Accid. Anal. Prev. 117, 381–391 (2018)
5.
Virdi, N., Grzybowska, H., Waller, S.T., Dixit, V.: A safety assessment of mixed fleets with connected and autonomous vehicles using the surrogate safety assessment module. Accid. Anal. Prev. 131(June), 95–111 (2019)CrossRef
6.
Elvik, R.: Area-wide urban traffic calming schemes: a meta-analysis of safety effects. Accid. Anal. Prev. 33(3), 327–336 (2001)CrossRef
7.
Shao, Y., Mohd Zulkefli, M.A., Sun, Z., Huang, P.: Evaluating connected and autonomous vehicles using a hardware-in-the-loop testbed and a living lab. Transp. Res. Part C Emerg. Technol. 102, 121–135 (2019)
8.
Hörl, S., Ruch, C., Becker, F., Frazzoli, E., Axhausen, K.W.: Fleet operational policies for automated mobility: a simulation assessment for Zurich. Transp. Res. Part C Emerg. Technol. 102, 20–31 (2019)CrossRef
9.
Pandey, V., Monteil, J., Gambella, C., Simonetto, A.: On the needs for MaaS platforms to handle competition in ridesharing mobility. Transp. Res. Part C Emerg. Technol. 108, 269–288 (2019)CrossRef
Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Trans. Intell. Transp. Syst. 12(2), 364–373 (2011)CrossRef
12.
Du, L., Han, L., Li, X.Y.: Distributed coordinated in-vehicle online routing using mixed-strategy congestion game. Transp. Res. Part B Methodol. 67, 1–17 (2014)CrossRef
13.
Dey, K.C., Rayamajhi, A., Chowdhury, M., Bhavsar, P., Martin, J.: Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network - performance evaluation. Transp. Res. Part C Emerg. Technol. 68, 168–184 (2016)CrossRef
14.
Farag, S.G., Hashim, I.H.: Safety performance appraisal at roundabouts: case study of Salalah City in Oman. J. Transp. Saf. Secur. 9(1), 67–82 (2017)
15.
Adegoke, E.I., Zidane, J., Kampert, E., Ford, C.R., Birrell, S.A., Higgins, M.D.: Infrastructure Wi-Fi for connected autonomous vehicle positioning: a review of the state-of-the-art. Veh. Commun. 20, 100185 (2019)
16.
Fu, R., Li, Z., Sun, Q., Wang, C.: Human-like car-following model for autonomous vehicles considering the cut-in behavior of other vehicles in mixed traffic. Accid. Anal. Prev. 132, 105260 (2019)
17.
Abdelkader, G., Elgazzar, K., Khamis, A.: Connected vehicles: technology review, state of the art, challenges and opportunities (2021)
18.
Datta, S.K., Da Costa, R.P.F., Harri, J., Bonnet, C.: Integrating connected vehicles in Internet of Things ecosystems: challenges and solutions. In: 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (2016)
19.
Xu, Z., Li, X., Zhao, X., Zhang, M.H., Wang, Z.: DSRC versus 4G-LTE for connected vehicle applications: a study on field experiments of vehicular communication performance. J. Adv. Transp. 2017 (2017)
20.
Aquino Santos, R., Edwards, A., Rangel Licea, V.: Wireless technologies in vehicular ad hoc networks: present and future challenges (2012)
21.
Bharati, S., Zhuang, W.: CAH-MAC: cooperative ADHOC MAC for vehicular networks. IEEE J. Sel. Areas Commun. 31(9), 470–479 (2013)CrossRef
22.
Abboud, K., Omar, H.A., Zhuang, W.: Interworking of DSRC and cellular network technologies for V2V communications: a survey. EEE Trans. Veh. Technol. I 65, 9457–9459 (2016). https://ieeexplore.ieee.org/document/7513432
23.
Choudhury, A., Maszczyk, T., Math, C.B., Li, H., Dauwels, J.: An integrated simulation environment for testing V2V protocols and applications. Procedia Comput. Sci. 80, 2042–2052 (2016)CrossRef
24.
Pettigrew, S., Fritschi, L., Norman, R.: The potential implications of autonomous vehicles in and around the workplace. Int. J. Environ. Res. Public Health 15(9) (2018)
25.
Clavet, J.‐P.: Public transit. Can. Public Adm. 11(1), 56–64 (1968)
Tian, D., et al.: Examining the safety, mobility and environmental sustainability co-benefits and tradeoffs of intelligent transportation systems. Nat. Center Sustain. Transp., 35 (2017)
28.
Singh, V., Kumar, P.: Web-based advanced traveler information system for developing countries. J. Transp. Eng. 136(9), 836–845 (2010)CrossRef
29.
Jiang, D., Delgrossi, L.: Towards an international standard for wireless access in vehicular environments. In: IEEE Vehicular Technology Conference (2008)
30.
Ahmed, F., Hawas, Y.E.: An integrated real-time traffic signal system for transit signal priority, incident detection and congestion management. Transp. Res. Part C Emerg. Technol. 60, 52–76 (2015)CrossRef
Hawas, Y.E., Sherif, M., Didarul Alam, M.: Optimized multistage fuzzy-based model for incident detection and management on urban streets. Fuzzy Sets Syst. 381, 78–104 (2020)
35.
Washington State Department of Transportation (WSDOT): Protocol for VISSIM Simulation, Washington State Department of Transportation, p. 162, September 2014. www.oregon.gov/odot/td/tp/apm/addc.pdf
36.
Dowling, R., Skabardonis, A., Alexiadis, V.: Traffic Analysis Toolbox Volume III : Guidelines for Applying Traffic Microsimulation Modeling Software. Rep. No. FHWA-HRT-04-040, U.S. DOT, Federal Highway Administration, Washington, D.C, vol. III, p. 146, July 2004
