2. Root Causes of Congestions
2.1. Recurring Congestion
- Bottlenecks and capacity: The most common cause of congestion is due to blockages, as shown in Figure 2. Bottlenecks generally occur during peak flow hours, where the number of lanes converging on a roadway, bridge, or tunnel exceeds the number of lanes these facilities have [38,39,40]. It may also occur when the demand exceeds the capacity of a road. The capacity of any road indicates the maximum amount of traffic that can be handled. Capacity can be determined by the number and width of lanes, merging length at interchanges, and roadway alignment.
- Insufficient infrastructure: Insufficient infrastructure is one of the most significant reasons for congestion, especially in highly populated areas. Because of the higher population rate, the number of vehicles also increases with it. When the existing number of infrastructures fails to occupy this increasing number of cars, congestion occurs .
- Variation in traffic flow: The variability in day-to-day traffic demands results in higher volumes in some days compared to others. When these variable demands do not match with the fixed capacity, a delay may occur .
- Inadequate traffic controllers: Poorly timed signals or designs of traffic controllers such as traffic lights, stop signs, speed reductions, or railroad crossings can disrupt a regular traffic flow, which leads to congestion and travel time fluctuation .
2.2. Nonrecurring Congestions
- Work zones: Work zones refer to the construction activities on the roadway by making physical changes to the highway environment. These changes lead to a reduction in the number or width of travel lanes, lane ‘shifts’, lane diversions, reduction or elimination of shoulders, and temporary roadway closures.
- Weather: Changes in environmental conditions or weather can affect traffic flow and driver behavior. These may also modify the traffic control systems, such as signals and railway crossing, as well as road conditions. Due to bad weather induced road conditions, about 28% of all highway crashes and 19% of all fatalities take place . Besides, both vehicle speed and volume can be affected by high wind-gust, heavy rains, or snow.
- Other special events: Demand variations of traffic flow about a particular event that generally differ from the usual flow pattern. These events include sports events (game day), concerts, or other social events. A sudden spike in traffic demand during special occasions can overwhelm the system and create congestion.
3. Current Approaches to Measure Congestion
3.1.1. Speed Reduction Index (SRI)
3.1.2. Speed Performance Index (SPI)
3.2. Travel Rate
3.3.1. Delay Rate
3.3.2. Delay Ratio
3.4. Level of Services (LoS)
3.5. Congestion Indices
3.5.1. Relative Congestion Index (RCI)
3.5.2. Road Segment Congestion Index (Ri)
3.6. Federal Congestion Measures
3.6.1. Congested Hours
3.6.2. Travel Time Index (TTI)
3.6.3. Planning Time Index (PTI)
3.7. Approaches in Different Countries
4. Evaluation of Current Approaches
4.1. Dataset Description
4.2. Data Analysis
4.2.1. Daily Data Analysis
4.2.2. Weekly Data Analysis
4.2.3. Case Study Discussion
5.1. Advantages and Disadvantages of Congestion Measures
5.2. Criteria of a Good Congestion Measure
- be well-defined, easily comprehensible, and uncomplicated for non-technical users to interpret the results easily,
- reflect the real level of service for any road types,
- consider different system performances, such as travel time and speed,
- provide a continuous range of values,
- be able to be used in predictive and statistical analysis purposes,
- offer comparable values to different road types, and
- be widely applicable for different road types
5.3. Current Mitigation Approaches
- Add more base capacity. The capacity of road infrastructure can be improved by increasing the number and size of highways, providing more transit and freight rail service, adding additional lanes, and building new highways [3,58,59,60]. However, this approach typically demands a substantial amount of implementation costs.
- Operate the existing infrastructures efficiently. The existing infrastructures can be utilized more efficiently by redesigning mitigation routes for specific bottlenecks, such as in the interchanges and intersections, to increase their function or their baseline capacity .
- Various-sector traffic management. For incident management, identifying accidents quickly, improving response times, and managing accidents or other incident scenes more effectively can help in reducing the event induced congestions [44,61]. For the work zone, managing traffic in a work zone area is necessary to reduce congestion, particularly at peak hours. Thus, the work zone should be planned cautiously [62,63]. Planning for special events ahead of time and coordinate with the traffic control plans may ease congestion [6,64]. Controlling traffic signals, ramp meters, and manage lane usage with a computerized system are often found to a practical approach in reducing congestion during peak hours . Different management protocols, such as travel demand management (TDM), non-automotive travel modes, and land use management, can be followed .
- Weather and traveler information. Predicting weather conditions in specific areas and roadways would be beneficial for travelers to be prepared for congestions [66,67]. Suggesting alternative routes for travelers ahead of the congestion period and area may reduce the volume-to-capacity ratio in the congestion area .
5.4. Potential Future Research Directions
- Resilient traffic management system. Resilient often defines as how fast an entity can recover from its disrupted states [68,69,70]. Since the recurring congestion is a cyclical scenario, the analysis of how quickly traffic congestion can return to its normal operating state without congestion will be beneficial in developing a resilient transportation management system [49,50,71]. Additionally, a resilience-based congestion measure for both recurring and nonrecurring congestions is an area that has a potential research scope. Several resilience-oriented congestion measures have been proposed for recurring congestion [49,50]. However, resilience-based measures for nonrecurring congestion is still underexplored.
- Analysis of nonrecurring congestion. A sustainable and resilient transportation system should be able to offer interrupted functionality during unexpected events. Due to the increased intensity and frequency of the natural-related disasters, nonrecurring congestion is a field of study that should be explored extensively in terms of measurement approach, predictive analysis, and uncertainty investigation [7,15,72,73,74]. The probability of occurring congestion varies for different types of events, where some events are completely unpredictable, such as incidents from vehicles crashes unexpected weather changes, and evacuation due to disasters [7,8,75,76].The pattern and probability occurrence of the root cause events for nonrecurring congestion are often uncertain. Thus, it is relatively difficult to control and manage the traffic accurately while the event is happening . Especially during disasters, when an evacuation is needed, traffic congestion is commonly encountered, which excessively slows down the evacuation process. Several efforts have been made to analyze the impact of congestion on evacuation and increase efficiency can be found in Refs. [8,43,77]. The pattern detection methods for nonrecurring congestion during evacuation for predictive analysis can be investigated further to reduce evacuation-related congestion.
- Smart traffic management system. The development of a sustainable and resilient transportation system may be achieved with the aid of technological advancements. As computational technologies advance rapidly, the conventional traffic management systems have evolved to become more intelligent with the help of IoT (Internet of Things). IoT-based traffic management systems and congestion mitigation techniques are often developed for smart urban areas [27,78,79]. Different data-driven approaches are employed to predict the time, probability, and the level of congestion [29,80,81,82]. However, congestion can still occur in many cases due to the deviation of prediction with implementation, or the improper usage of predicted features. In the future, these issues should be addressed.The research on smart traffic management can also be leveraged to include the nonrecurring congestion scenario, for example, in predicting the most effective time of implementing evacuation or other mitigation approaches [8,77]. In addition to smart traffic management systems, the retrofit process of current infrastructures, for example, to accommodate the rapidly increasing charging stations and electric vehicles on roads, is another prospective future research area towards the development of a sustainable transportation system .
- Social-environmental effects of congestion. The irreversible environmental impacts on congestion are constantly increasing day-by-day. Different advanced sustainability approaches have been developed to reduce fuel consumption and greenhouse gas emission due to congestion [25,84,85]. Various strategies that fall under the intelligent transport system (ITS) categories, such as adaptive traffic light control systems [86,87], have been proposed but sparsely implemented. The impacts of such methods are believed to be able to reduce the negative environmental effects as well as provide pollution-free air by reducing congestion significantly. Over recent years, electric vehicles have become a popular potential option to combat carbon emissions. However, there are also other environmental impacts imposed by batteries from electric vehicles [88,89]. Thus, proper recycling procedures should be researched further [90,91,92].
- Socioeconomic effects of congestion. Traffic congestion significantly affects urban life from both social and economic aspects [9,93]. The causes and consequences of urban traffic congestion have been considerably explored . The overall productivity of society reduces due to traffic congestion, which, in result, affects the economy as well [13,58]. The socioeconomic aspects should be incorporated in the congestion mitigation research so that the negative socioeconomic impacts of congestion can be reduced.
Conflicts of Interest
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|Speed Performance Index||Traffic State Level||Description of Traffic State|
|(0,25)||Heavy congestion||Low average speed, poor road traffic state|
|(25,50)||Mild congestion||Lower average speed, road traffic state bit weak|
|(50,75)||Smooth||Higher the average speed, road traffic state better|
|(75,100)||Very smooth||High average speed, road traffic state good|
|LoS Class||Traffic State and Condition||V/C Ratio|
|B||Stable flow with unaffected speed||0.61–0.70|
|C||Stable flow but speed is affected||0.71–0.80|
|D||High-density but the stable flow||0.81–0.90|
|E||Traffic volume near or at capacity level with low speed||0.91–1.00|
|Sunday||a.m.||12:10, 8:20||2||12:10, 6:40, 8:01, 8:20, 11:20||5|
|p.m.||7:31, 7:01, 4:50, 2:31, 2:10, 1:40, 12:50||7||12:50,1:40, 2:10–2:31, 3:31, 4:01, 4:31, 4:50, 5:40, 7:01, 7:31, 9:40||11|
|Monday||a.m.||-||12:20, 1:20, 7:31, 4:51, 10:40, 11:50||6|
|p.m.||2:10, 3:01, 3:40, 4:40, 5:31, 10:20||6||1:21, 2:10, 3:01–4:10, 4:40, 5:31–5:50, 6:31–6:40, 10:20||7|
|Tuesday||a.m.||6:21,7:21, 10:50||3||6:01, 6:21, 7:10,7:21, 7:40, 7:51, 10:50, 11:01||8|
|p.m.||12:31, 2:31, 2:50, 3:31–3:50, 5:01–5:31, 7:50||6||12:31, 1:10, 2:31, 3:01, 3:31–4:01, 5:01–5:31, 6:10, 7:50, 8:01, 8:20, 8:40, 11:50||12|
|p.m.||2:10, 4:01–4:40, 5:10, 5:40, 6:31||5||1:10, 2:10–2:21, 3:01, 4:01–4:40, 5:10, 5:21, 5:40, 6:31||8|
|Thursday||a.m.||4:10, 8:31–8:40, 10:50, 11:01, 11:40||5||7:31, 7:40, 8:31, 8:40, 10:01, 10:50, 11:01, 11:40||8|
|p.m.||1:01, 3:10–3:21, 4:31, 5:31, 6:10, 7:50, 9:01||7||12:21, 1:01, 1:40, 3:10, 3:21, 3:50, 4:31, 5:10, 5:31, 5:50–6:10, 7:50, 9:01, 9:10||13|
|Friday||a.m.||4:01, 6:40–6:50||2||5:40, 6:40, 6:50, 7:21, 7:51, 8:11, 9:01, 9:21, 10:10||9|
|p.m.||3:01, 3:30, 4:11, 6:50, 7:50||5||12:01, 2:31, 3:01, 3:30, 4:11, 5:30, 5:40, 6:40, 6:50, 7:50, 9:20, 11:40||12|
|Saturday||a.m.||6:40, 8:01, 11:01,11:10||4||5:20, 6:40, 8:01, 10:20, 11:01, 11:10 11:40||7|
|p.m.||3:10, 3:40, 6:10, 9:01, 10:20||5||2:01, 3:10, 3:40, 5:31, 6:10, 9:01, 10:20, 11:31||8|
|Category||Measurement Approach||Congestion Range||Advantages||Disadvantages|
|Speed||Speed reduction index (SRI)||>4||Easily comprehensible |
Provides information about relative vehicle speed in normal and congested condition
|Does not consider nonrecurring conditions|
|Speed performance index (SPI)||Different range levels|
|Travel time||Travel rate||No range available||Both time and space are accounted for||Capacity is not included|
|Delay||Delay rate||No range available||Can be used to estimate system performance and choose efficient travel method||Limited for a specific road type |
No suggested congestion range
|Delay ratio||No range available||Compares relative congestion levels in different types of roads|
|Level of services (LoS)||Volume to capacity ratio||Different range levels||Comprehensible by non-technical users||Cannot provide continuous congestion value |
No information on speed and time are considered
|Congestion indices||Relative congestion index (RCI)||>2||Spatial-mean performance of traffic is represented||Limited to particular road type|
|Road segment congestion index||No range available||Appropriate to represent segment condition||Only applicable to measure specific segment conditions.|
|Federal||Congested Hours||No range available||Provides an estimation of the congested time period||Only depends on the speed|
|Travel time index (TTI)||No range available||Accounts for recurring congestion |
Both time and space are considered
|Value could vary due to different peak period consideration|
|Planning time index (PTI)||No range available||Describes travel time reliability to planners as well as network users||Planning for additional travel time might not always be reliable|
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