Non-recurrent Traffic Congestion Research Articles

Modeling spatiotemporal heterogeneity in interval-censored traffic incident time to normal flow by leveraging crowdsourced data: A geographically and temporally weighted proportional hazard analysis

Non-recurrent traffic congestion arising from traffic incidents is unpredictable but should be addressed efficiently to mitigate its adverse impacts on safety and travel time reliability. Numerous studies have been conducted about incident clearance time, while the recovery time, due to the limitations of data collection, is often inadvertently neglected in assessing incident-induced duration (i.e., the time from incident occurrence to the normal flow of traffic). Overlooking the recovery time is likely to underestimate the total incident-induced impact. Furthermore, the spatiotemporal heterogeneity of observed factors is not adequately captured in incident duration models. To address these gaps, this study specifically investigated traffic crashes as they reflect safety issues and are the primary cause of non-recurrent congestion. The emerging crowdsourced traffic reports were harnessed to estimate crash recovery time, which can complement the blind zone of fixed detectors. A geographically and temporally weighted proportional hazard (GWTPH) model was developed to untangle factors associated with the interval-censored crash duration. The results show that the GWTPH model outperforms the global model in goodness-of-fit. Many factors present a spatiotemporally heterogeneous effect. For example, the global model merely revealed that deploying dynamic message signs (DMS) shortened the crash time to normal. Notably, the GWTPH model highlights an average reduction of 32.8% with a standard deviation of 31% in time to normal. The study's findings and application of new spatiotemporal techniques are valuable for practitioners to localize strategies for incident management. For instance, deploying DMS can be very helpful in corridors when incidents happen, especially during peak hours.

Spatiotemporal clustering for the impact region caused by a traffic incident: an improved fuzzy C-means approach with guaranteed consistency

Traffic incidents disrupt the normal flow of vehicles and induce nonrecurrent traffic congestion. It has been well accepted that the shape of the spatiotemporal region impacted by a traffic incident should be consistent with the propagation of shockwaves. Although there has been a variety of approaches that attempt to estimate the impact region of traffic incidents, most of them are not capable of producing results with guaranteed consistency. In this research, we propose an improved fuzzy clustering approach that integrates the domain knowledge of shockwave theory for freeway incidents to address this issue, which is new to the literature. Compared to the general clustering approaches, our improved fuzzy clustering approach takes control of the clustering process by leveraging the directional propagation of shockwaves in the form of constraints, which can guarantee the consistency. In addition, unlike existing studies that employ discrete variables to distinguish traffic status in case of traffic incidents, the fuzzy clustering approach uses the continuous variable to indicate the incident impact on vehicle speed. This can help to reduce the information loss and estimate the impact region more accurately. Numerical experiments are conducted to evaluate the performance of our approach using both simulation and real data. Results show that our approach is able to guarantee that the shape of the impact region is consistent with the propagation of shockwaves and achieve higher accuracy of the estimated delay induced by the incident than the current state-of-the-art approach.

Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation

Predicting the duration of traffic incidents is a challenging task due to the stochastic nature of events. The ability to accurately predict how long accidents will last can provide significant benefits to both end-users in their route choice and traffic operation managers in handling of non-recurrent traffic congestion. This paper presents a novel bi-level machine learning framework enhanced with outlier removal and intra–extra joint optimisation for predicting the incident duration on three heterogeneous data sets collected for both arterial roads and motorways from Sydney, Australia and San-Francisco, U.S.A. Firstly, we use incident data logs to develop a binary classification prediction approach, which allows us to classify traffic incidents as short-term or long-term. We find the optimal threshold between short-term versus long-term traffic incident duration, targeting both class balance and prediction performance while also comparing the binary versus multi-class classification approaches using quantiled duration groups and varying threshold split. Secondly, for more granularity of the incident duration prediction to the minute level, we propose a new intra–extra Joint Optimisation algorithm (IEO-ML) which extends multiple baseline ML models tested against several regression scenarios across the data sets. Final results indicate that: (a) 40–45 min is the best split threshold for identifying short versus long-term incidents and that these incidents should be modelled separately, (b) our proposed IEO-ML approach significantly outperforms baseline ML models in 66% of all cases showcasing its great potential for accurate incident duration prediction. Lastly, we evaluate the feature importance and show that time, location, incident type, incident reporting source and weather at among the top 10 critical factors which influence how long incidents will last.

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Traffic congestion mechanism in mega-airport surface

In this paper, guided by macroscopic cell transmission models, a cell transmission model of aircraft traffic flow on runways, taxiways and aprons was established to deduce and analyze the evolution rules of an airport surface traffic. The model was simulated by adopting data from a large domestic airport in China. The relationship between three parameters of traffic flow on taxiways was evaluated, and the airport surface traffic flow characteristics were analyzed. Moreover, this paper macroscopically analyzed the characteristics of spatial–temporal distribution of airport surface recurrent and non-recurrent traffic congestion. Simulation results showed that arrival rates and pushback rates had significant influence on the formation and dissipation of recurrent traffic congestion in different phases of the surface aircraft traffic flow. When the arrival rate was 0.3 aircraft/minute and the pushback rate was 0.23 aircraft/minute, the speed of aircraft on taxiways was high, the density and flow were large, and the aircraft had a high degree of obstacle-free taxiing. At this time, the airport ran smoothly and the surface network operation efficiency was high. Compared with the arrival rate, the outward expansion trend of each parameter was more obvious when the pushback rate changed. Parallel taxiways are less robust than apron taxiways and connect taxiways.

Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model

Nonrecurrent traffic congestion (NRTC) usually brings unexpected delays to commuters. Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The advancement of road traffic detectors provides researchers with a large-scale multivariable temporal-spatial traffic data, which allows the deep research on NRTC to be conducted. However, it remains a challenging task to construct an analytical framework through which the natural temporal-spatial structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC. In this paper, we present a novel analytical training-free framework based on the coupled scalable Bayesian robust tensor factorization (Coupled SBRTF). The framework can couple multivariable traffic variables including traffic flow, road speed, and occupancy through sharing the same sparse structure. Moreover, it naturally captures the high-dimensional temporal-spatial patterns of the traffic data by tensor factorization. With its entries revealing the distribution and magnitude of NRTC, the shared sparse structure of the framework compasses sufficiently abundant information about NRTC. While the low-rank part of the framework, expresses the distribution of general expected traffic conditions as an auxiliary product. Experimental results on real-world traffic data show that the proposed method outperforms the NRTC detection models based on the coupled Bayesian robust principal component analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and standard normal deviates (SND). The proposed method performs even better when only traffic data in weekdays are utilized, and hence can provide more precise estimations of NRTC for daily commuters.

Machine Learning Approach to Forecast Work Zone Mobility using Probe Vehicle Data

The aim of deploying intelligent transportation systems (ITS) is often to help engineers and operators identify traffic congestion. The future of ITS-based traffic management is the prediction of traffic conditions using ubiquitous data sources. There are currently well-developed prediction models for recurrent traffic congestion such as during peak hour. However, there is a need to predict traffic congestion resulting from non-recurring events such as highway lane closures. As agencies begin to understand the value of collecting work zone data, rich data sets will emerge consisting of historical work zone information. In the era of big data, rich mobility data sources are becoming available that enable the application of machine learning to predict mobility for work zones. The purpose of this study is to utilize historical lane closure information with supervised machine learning algorithms to forecast spatio-temporal mobility for future lane closures. Various traffic data sources were collected from 1,160 work zones on Michigan interstates between 2014 and 2017. This study uses probe vehicle data to retrieve a mobility profile for these historical observations, and uses these profiles to apply random forest, XGBoost, and artificial neural network (ANN) classification algorithms. The mobility prediction results showed that the ANN model outperformed the other models by reaching up to 85% accuracy. The objective of this research was to show that machine learning algorithms can be used to capture patterns for non-recurrent traffic congestion even when hourly traffic volume is not available.

Optimizing the selection and scheduling of multi-class projects using a Stackelberg framework

One of the major causes of non-recurrent traffic congestion in urban areas is the implementation of transport infrastructure projects on city roads. The seeming ubiquity of work zones in cities causes road user frustration and safety hazards, and public relations problems for the transport agency. For this reason, transport agencies seek strategic ways to not only select urban projects but also schedule them in a manner that minimizes the effort associated with these functions. In other words, they seek to exploit the synergies between the tasks of project selection and project scheduling while duly accommodating the project interdependencies. This study introduces a general framework that simultaneously and optimally selects and schedules urban road projects subject to budgetary constraints over a given planning horizon. The project classes considered in this study are lane addition, new road construction, and road maintenance. Through a mimicry of the classic Stackelberg leader-follower game, this problem is formulated herein as a bi-level program. In the upper level, the leader (transport agency decision-makers) determines an optimal set of projects from a larger pool of candidate projects and decides an optimal schedule for their implementation. In the lower level, the followers (road users) seek to minimize their travel delays based on the two decisions made by the leader in the upper level. The numerical experiments show that if the decision-makers do not consider the peri-implementation capacity reduction, the resulting set of selected projects and their construction schedule can lead to significant travel delay cost for the road users.

Analysing the root cause of the damage risk in heavy vehicles to reduce traffic congestion

This study aims to investigate important damage risks in heavy vehicles on the highway using risk analysis method as well as to examine their root causes using Fault Tree Analysis (FTA). This is important as the basis for formulating the solution to solve the Non-Recurrent traffic Congestion (NRC) problems which often happens in Surabaya's arterial roads. Data was collected through series of research steps consisting preliminary survey, questionnaire survey as well as expert's interview survey. Five top risk events were obtained from risk analysis using Probability-Impact Matrix, namely flat tire, engine failure, damage on the axle, damage on the leaf spring, and accident. Meanwhile, 25 basic events were obtained as the root cause the five high risks based on FTA method. Eight highest basic events are overloading, poor maintenance, inadequate periodic vehicle inspection, potholes on road, poor road surface, inadequate or improper pre-operational check, driver behavior, and the road up or down steeply. This paper contributes to the body of knowledge by proposing a framework to investigate NRC problem due to damage risks in the heavy vehicle by combining risk analysis and FTA methods to obtain a comprehensive understanding about this problem and to create a more sustainable road transportation.

Simulation and analysis of non-recurrent traffic congestion triggered by crashes in road networks using a grid-based approach

The non-recurrent traffic congestion triggered by crashes is one of the most important factors that undermine the traffic efficiency of urban road networks. In this article, an improved cellular automaton model was proposed to simulate the non-recurrent congestion triggered by crashes in grid networks with signalized intersections. Four rules were adopted to represent vehicle movements on road sections and intersections. The network speed is adopted to capture the propagation and dissipation of the non-recurrent congestion. The effect of main influencing factors of crashes on the road network was evaluated through the simulation. Simulation results showed the incident duration and areas affected by the distance between the crash point and the upstream intersection, the number of closed lanes, and the crash duration. In addition, the stop-start wave was observed in the simulation. The realistic findings from the simulations validated the model to have the potential for practical applications in the analysis of the non-recurrent congestion triggered by crashes.

Traffic Speed Prediction Under Non-Recurrent Congestion: Based on LSTM Method and BeiDou Navigation Satellite System Data

The full utilization of Location-Based Vehicle Sensor Data (LB-VSD) can improve the efficiency of traffic control and management. Currently, LB-VSD is widely applied to the prediction of traffic speed. Like the GPS system, BeiDou satellite navigation system (BDS) can collect LB-VSD. In China, the key operation vehicles on the expressway are equipped with BDS to monitor the travel path. This provides a basis for predicting the traffic speed on expressway accurately. In this paper, considering the abnormal data collected by BDS, the screening and processing rules are made, and then the traffic speed sequence is extracted. Considering the data-missing problem caused by equipment failure or abnormal data elimination and the data sparse problem caused by small size of sample, a filling method based on trend-historical data is proposed. Traffic flow evolution is a complex process. Sudden accidents or bad weather can cause a sudden change in traffic flow and non-recurrent traffic congestion. The prediction accuracy of traditional machine learning methods is low when non-recurrent congestion occurred. In order to solve this problem, this paper adopts a deep learning model?Long Short-Term Memory (LSTM) to predict the traffic speed. Moreover, three-regime algorithm is used while building the prediction model. The prediction method is compared with Support Vector Regression (SVR) method. The results show that the prediction accuracy of the proposed method is higher than that of SVR algorithm, and the robustness is better in the case of non-recurrent traffic congestion.

Identification of Critical Factors for Non-Recurrent Congestion Induced by Urban Freeway Crashes and Its Mitigating Strategies

Given the extreme difficulty of estimating crash likelihoods, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. Such factors must be identified to develop crash management strategies and congestion management strategies. The objectives of this study are to identify causal factors that affect non-recurrent congestion and to propose some operational strategies for mitigating crash-induced non-recurrent traffic congestion. To achieve these objectives, a case study was conducted to identify spatiotemporal non-recurrent congestion regions using a previously developed method based on historical inductance loop detector data collected from six major freeways in Orange County, California. Based on the case study results, potentially significant factors in non-recurrent congestion were identified using the Cox proportional hazard model. Additionally, with the factors identified as significant, operational strategies were proposed for mitigating non-recurrent congestion due to freeway crashes.

Congestion by accident? A two-way relationship for highways in England

This paper aims to estimate the causal effect of accidents on traffic congestion and vice versa. In order to identify both effects of this two-way relationship, I use dynamic panel data techniques and open access ‘big data’ of highway traffic and accidents in England for the period 2012–2014. The research design is based on the daily-and-hourly specific mean reversion pattern of highway traffic, which can be used to define a recurrent congestion benchmark. Using this benchmark, I am able to identify the causal effect of accidents on non-recurrent traffic congestion. A positive relationship between traffic congestion and road accidents would yield multiplicative benefits for policies that aim at reducing either of these issues. Additionally, I explore the duration of the effect of an accident on congestion, the ‘rubbernecking’ effect, as well as heterogeneous effects in the most congested highway segments. Then, I test the use of methods which employ the bulk of information in big data and other methods using a very reduced sample. In my application, both approaches produce similar results. Finally, I find a non-linear negative effect of traffic congestion on the probability of an accident.

HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

Abstract. Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

Non-recurrent traffic congestion detection on heterogeneous urban road networks

This paper proposes two novel methods for non-recurrent congestion (NRC) event detection on heterogeneous urban road networks based on link journey time (LJT) estimates. Heterogeneity exists on urban road networks in two main aspects: variation in link lengths and data quality. The proposed NRC detection methods are referred to as percentile-based NRC detection and space–time scan statistics (STSS) based NRC detection. Both of these methods capture the heterogeneity of an urban road network by modelling the LJTs with a lognormal distribution. Empirical analyses are conducted on London's urban road network consisting of 424 links for the 20 weekdays of October 2010. Various parameter settings are tested for both of the methods, and the results favour STSS-based NRC detection method over the percentile-based NRC detection method. Link-based analyses demonstrate the effectiveness of the proposed methods in capturing the heterogeneity of the analysed road network.

Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences

Traffic control agencies (TCAs), including police officers, firefighters, or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate nonrecurrent traffic congestion caused by planned and unplanned events. Understanding and predicting TCA behaviors is significant to optimize event traffic management and operations. In this paper, we propose a pressure-based human behavior model to mimic TCA's decision-making behavior. The model calculates TCA's pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA's pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Further, we test the model with videotaped TCA behavior data at a real-world intersection. Both validation results support the effectiveness of proposed behavior model.

Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on urban road networks. In this paper we propose an NRC detection methodology to support the accurate detection of NRCs on large urban road networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined as those LJTs that are greater than a threshold. The threshold is calculated by multiplying the expected LJTs with a congestion factor. To evaluate the effectiveness of the proposed NRC detection method, we propose two novel criteria. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be associated with incidents. The proposed NRC detection methodology is tested for London’s urban road network. The optimum value of the congestion factor is determined by sensitivity analysis by using a Weighted Product Model (WPM). It is found out those LJTs that are at least 40% higher than their expected values should belong to an NRC; as such NRCs are found to maintain the best balance between the proposed evaluation criteria.

Performance Measures of Manual Multimodal Traffic Signal Control

Representatives of traffic control agencies (TCAs)—including police officers, firefighters, and other traffic law enforcement officers who override automatic traffic signal controls—are crucial to the mitigation of nonrecurrent traffic congestion caused by planned and unplanned events. An unanswered question is how well TCA officers perform compared with state-of-practice automatic traffic signal controls. This study assessed the performance of TCA manual multimodal traffic signal control during special events. First, an interview was designed to promote understanding of the control rules of TCAs and the practice of manual traffic signal control. Next, a simulation experiment was conducted to record control actions during multimodal traffic flows containing buses, pedestrians, and passenger cars. Third, a TCA performance index was developed through a comparison of manual control with actuated signal control and optimal control solutions from an online optimization model, which assumed the availability of rich vehicle information, to determine the best control strategies. The results show that manual traffic control can significantly improve control performance, even to the point of approaching the performance of the optimized timing plan. Large variations, however, were observed during the study.

Microscopic traffic behaviour modelling and simulation for lane-blocking arterial incidents

Incident-induced driver behaviour modelling is essential to analyse non-recurrent traffic congestion problems. However, such research is inadequate in such areas as traffic flow prediction, traffic simulation and incident management. This article presents microscopic lane traffic models to characterise incident-induced driver behaviour including car following and lane changing conducted under conditions of lane-blocking arterial incidents. To demonstrate the validity of the proposed models, a specific microscopic traffic simulation program embedded with the proposed incident-induced lane traffic behaviour models is tested by comparing simulation data with video-based incident data collected from five incident events. Preliminary test results indicate that the proposed microscopic traffic behaviour models permit not only reproducing incident-induced traffic behaviour but also characterising incident effects on lane traffic phenomena.