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Accurate and timely short-term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to complex non-linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non-linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper-parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.

This paper focuses on the resilience of rail freight operations when affected by extreme weather events. Such events, most likely linked to climate change, are becoming more common and it is vital to mitigate their effects on freight transport activity. Based on a British case study of rail network disruption resulting from a key line closure in early-2016, the analysis considers the impacts on rail freight service provision and the wider supply chains. Following a review of the relevant literature, the case study is analysed using data from a combination of sources including an annual rail freight database, open access real-time train running data, observation surveys and stakeholder interviews. This reveals widespread consequences of the disruption, with fewer freight trains operated than normal, and longer and less punctual journeys for those which ran. However, despite the considerable disruption during the period of the line closure itself, there has been no discernible long-term impact on the rail freight flows which were impacted by the closure. The insight provided by the analysis is used to make a series of recommendations to the rail industry and policy makers.

Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers’ paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

Locating the surrounding vehicles is an important environment perception task for autonomous vehicles and advanced driver assistance systems. This task is usually explored based on the sensors’ pre-calibration (e.g. height or pitch angle), but can be challenging when the calibration fails (e.g. on the sloping and uneven roads). In this work, we propose a calibrated feature-point based (CFPB) method to estimate the car-following distance adaptive to rough roads, using a single camera. Instead of using the pre-calibrated parameters, CFPB method is based on the surrounding vehicles’ feature points. It benefits from the fixed coordinate relations among these points, which enables the algorithm to be adaptive to rough roads. These fixed coordinate relations can be calibrated during driving. Namely, after a few seconds of observation to a surrounding vehicle, the CFPB method can start working for more accurate estimation. Furthermore, the proposed algorithm takes the perspective-n-point method as the framework. YOLO V3 and scale-invariant feature transform are applied as the vehicle detector and feature point extractor. The feature point calibration is dynamically updated and the results are smoothened by a Kalman filter. The camera is chosen because of the good performance on objects detection and feature extraction. The proposed algorithm is evaluated on a real-world road with dynamic traffic flow. Mobileye, a widely used car-following distance estimator on AV, is installed during the tests as the benchmark. The results indicate that the proposed method achieves decimeter-level accuracy and outperforms the Mobileye system in cases where the road slope changes significantly.

This research investigates the influence of decision-maker behavior on policies that may be adopted for the protection of highway infrastructure against inundations resulting from sea-level rise. We develop an integrated game-theoretical decision-making framework to represent multiple co-opetitive decision-makers’ behavior and use the San Francisco Bay Area shoreline with a scenario of a 0.5 m sea-level rise as a numerical simulation study. The decision-makers’ objective is to minimize the traffic delay caused by inundations in the transportation network that lies within their geographical boundaries. Each decision-maker should determine where to build levees either only along their shoreline without cooperation or along a shared shoreline within a coalition. In this framework, each competitive decision-maker can consider cooperation to minimize its traffic delay, so its behavior can be defined as co-opetitive. We define necessary conditions for forming coalitions for multiple co-opetitive decision-makers, as well as cost-distributing rules and incentive negotiation processes within each coalition. Our model considers the effects of hydrodynamic interactions, traffic flow patterns changes as a result of inundations, and budget constraints on the costs of seashore protection. The hydrodynamics in the Bay Area are affected by the shoreline protection strategy, and closure of a highway link in one county affects traffic delays in other counties due to traffic re-routing. Thus, protection decisions made by a county have potential impacts on several other counties, and therefore counties must consider other counties’ actions. In the numerical study, we investigate the results of co-opetitive games for a range of funding scenarios. It is shown, through examples, that cooperation among counties decreases the additional delay for all participants in most cases compared to competition-only cases. In some cases, cooperation also reduces protection costs.

Traffic jams occur even without bottlenecks, simply because of interaction of vehicles on the road. From a driver’s point of view, the instability of the traffic flow arises stochastically. Because...

Traffic pattern analysis is an active and essential part of transportation research. When traffic condition is adverse and unprecedented, traffic sequences are useful in the analysis of traffic behaviour. The sequence through which traffic congestion has arisen can be predicted using sequence rules from the generated traffic sequence. This work aims at mining traffic sequence pattern and prediction of traffic volume based on traffic sequence rules. To mine peak hour traffic sequences in order to make better travel decision, travel time based PrefixSpan (TT-PrefixSpan) algorithm is proposed to analyse traffic flow on highways. As a result, the prediction of traffic volume is effected by the generated traffic sequences. Such analysis would pave the way for devising data driven computational methods in reducing traffic congestion. Real-time traffic volume data for 53 weeks is collected at a centralised toll system comprising toll collections centres at three different sites. To show the significance of this problem-solving approach, TT-PrefixSpan is experimented on three different sites. The extraction of a frequent traffic sequence pattern is reported with experimental analysis. The evaluation of different traffic condition present at each site has shown promising results. Towards the end, a summary of results is presented with directions for future research.

Travel time estimation plays an important role in freeway performance assessment and reliability management. Conventional estimation methods based on speed information do not consider the level of congestion during modelling, leading to unreliable estimations under congested conditions. This study presents a freeway travel time estimation method based on the general motors (GM) model, where the concept ‘virtual vehicle’ was proposed to apply macroscopic traffic flow data to the microscopic GM model. First, a GM-based travel time estimation (GMTTE) model was developed to estimate link travel time. Three sets of parameters l , m and α were separately used under free-flowing, congestion and transition conditions. Next, two corridor estimation models, namely GMTTE time-slice-based (GMTTE-TSB) model and GMTTE continuous-speed (GMTTE-CS) model, were proposed. To calibrate the three parameters, a genetic algorithm framework was developed. The proposed method was calibrated and evaluated using traffic data collected from seven freeway segments in the greater St. Louis, Missouri region. Results show that (i) the GMTTE-CS model outperformed the GMTTE-TSB model; (ii) the optimal parameters of the GMTTE-CS model under free-flowing and congested conditions are l = 1.1 , m = 2.0 , α = 8.0 and l = 1 , m = 0.1 , α = 8.0 , respectively; (iii) the GMTTE-CS model outperformed the instantaneous and time-slice models under congested conditions, while showing similar accuracies with the two models under free-flowing and status-transition conditions; and (iv) the optimal time interval was found to be 9–10 min for free-flowing conditions and 6–7 min for congested conditions.

To improve the capacity of signalised intersections for left-turn vehicles, an unconventional left-turn design named the contraflow left-turn lane is applied at intersections in several cities of China. The main concept of the design is to provide more capacity for left-turn vehicles by dynamically making use of the adjacent opposite lanes. In this study, a cellular automaton model that simulates left-turn traffic flow at a signalised intersection with a contraflow left-turn lane was developed and verified by field data. Rules for vehicles entering the contraflow left-turn lane were proposed, and various factors including the vehicle type, driver type, and the ratio of U-turn vehicles were considered. The simulation results showed that the contraflow left-turn lane could increase the capacity of the intersection and decrease the delay of left-turn vehicles. Also, the optimal match between the length of the contraflow left-turn lane and the duration of the pre-signal green light can be evaluated via simulation. This study can provide a reference for the actual application of contraflow left-turn lanes.

Nowadays, the effectiveness of any smart transportation management or control strategy would heavily depend on reliable traffic data collected by sensors. Two problems regarding sensor data quality have received attention: first, the problem of identifying malfunctioning sensors; second, reconstruction of traffic flow. Most existing studies concerned about identifying completely malfunctioning sensors whose data should be discarded. In this paper, we focus on the problem of error detection and data recovery of partially malfunctioning sensors that could provide valuable information. By integrating a sensor measurement error model and a transportation network model, we propose a Generalized Method of Moments (GMM) based estimation approach to determine the parameters of systematic and random errors of traffic sensors in a road network. The proposed method allows flexible data aggregation that ameliorates identification and accuracy. The estimates regarding both systematic and random errors are utilized to conduct hypothesis test on sensor health and to estimate true traffic flows with observed counts. The results of three network examples with different scales demonstrate the applicability of the proposed method in a large variety of scenarios.

This study presents a method of mathematical programs with equilibrium constraints (MPEC) to determine the optimal sites of street parking facilities in a working area where drivers travel through or cruise for parking spaces in the morning commute. The optimal sites of street parking facilities incur shortest queuing delay in the capacitated network. We first propose a variational inequality to formulate the heterogeneous quantal response equilibrium (HQRE) for parkers in non-cooperative parking game and the logit-based stochastic user equilibrium (SUE) for travelers traversing the area. The HQRE is developed under the “infinite cyclic flows” assumption, allowing all possible routes, containing the cyclic routes of all times including the infinite. Therefore boundedly rational parkers who search the same curb for more than once via cyclic cruising strategies can be modeled in the parking game. Then based on the equilibrium conditions, the MPEC was developed to minimize the total queuing delay for all drivers in the area. The radial basis function (RBF) model algorithm, one of the surrogate model-based algorithms, was proposed to iteratively solve the MPEC problem. A numerical example from a real working area is presented to illustrate the proposed MPEC problem. The numerical results show that the traffic flow increase caused by cruising for parking can be presented by our model. In addition, the appropriate allocation should avoid parking spaces at the curbs of arterial where travelers drive for traversing the area and the streets intersecting the arterial.

According to the World Health Organization (WHO), Iran is one of the countries with the highest rates of road deaths in the world. About 18,000 people die every year in road accidents in Iran, where about 22 percent of the dead are pedestrians. The purpose of the present paper is to investigate the effective factors on performance of drivers during interaction with pedestrians. Therefore, the Naturalistic Driving Study (NDS) of behaviour of 66 participants (29 males and 27 females, 18–65 years old) has been evaluated in Babol city, Mazandaran province, during 2014–2016. The behavioral studies of the participants were conducted in 216 cases of vehicle-pedestrian interaction in divided road and 485 cases in undivided road through video-recorded process. The results showed that vehicle speed and distance to pedestrians are the most important factors affecting the occurrence of vehicle-pedestrian interaction in both sites. Moreover, the results show that on the divided road, Running when crossing the street by pedestrians, as well as listening to the music by drivers, increases the possibility of interaction. Also, on the undivided road, the attention to the traffic flow of road before crossing by pedestrians, as well as crossing the street in a group increase the probability of driver's performance. Drivers responded to pedestrian crossing the street by some performances such as decreasing speed, changing the line, stop and acceleration collision. Finally, the probabilistic models of driver performance as well as the type of performances based on the variables affecting the behavior of drivers are determined using the binary logit model and multinomial logit model, respectively. Further model validation and transferability were checked and it has been observed that the driver performance and types of performance models developed in this study represents quite well. The inference of these models will be useful to assess Drivers’ behavioral models and suggest automotive assistance systems for improving pedestrian safety.

Advanced autonomous vehicle technology is suggested to offer a unique solution to many of the current problems in road transport. This paper studies the impact of the transition in automated driving capabilities (driving mode choice) on road congestion pricing and vice versa, accounting for the interdependencies between traffic flow, the choice level of autonomous driving, effective road capacity and marginal travel cost. It is shown that (in a more or less distant future) when drivers can choose among various levels of car automation during a trip and when shortcomings associated with autonomous driving – e.g. adverse effects regarding privacy, software hacking, liability, safety, driving pleasure, manual control recovery – are non-negligible, their generalized user cost curve is the lower envelope of a swarm of traffic flow dependent individual cost curves whose slope is determined by the level of autonomous driving. This has several implications for our understanding of Pigouvian road congestion pricing. Most importantly, marginal social trip costs are then no longer convex and strictly increasing in traffic flow even when traffic is only ‘normally congested’, thereby introducing the possibility of multiple congestion pricing equilibria even in the absence of what is usually called ‘hypercongestion’. When inconveniences related to autonomous driving are sufficiently high, the imposition of congestion tolls may lead to a situation where drivers abandon autonomous technologies entirely and opt instead for fully manual driving. Our findings suggest that the emergence of advanced autonomous driving capabilities and the transition from manual to automated control on the road will probably make congestion pricing more, not less, complex for researchers and policymakers.

Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field.

This research investigates the influence of decision-maker behavior on policies that are likely to be adopted for the protection of highway infrastructure against inundations resulting from sea level rise. We analyze two different types of games to represent decision-maker behavior, and use the San Francisco Bay Area shoreline with a scenario of a 0.5 m sea level rise as a case study, which is expected in 2054. In our model, the objective of the decision-makers (the counties bordering the SF Bay Area) is to minimize the traffic delay caused by inundations in the transportation network that lies in the geographical boundaries of their counties. Our model considers hydrodynamic interactions, traffic flow patterns changes as a result of inundations, and budget constraints on the protection costs. The hydrodynamics in the Bay Area are affected by the shoreline protection strategy: protection of the shoreline of a county may lead to increased inundations in another, unprotected, county. Furthermore, closure of a highway link in one county affects traffic delays in other counties due to traffic re-routing. Thus, protection decisions made by a county have potential impacts on several other counties, and therefore counties must take into account other counties' actions. Both competitive (Nash) and cooperative games are analyzed. A Nash equilibrium is achieved among the counties when each acts to minimize its objective without the possibility of collaborating with other counties. We compare the results of Nash games with possible cooperation strategies for a range of funding scenarios. It is shown, through several examples, that cooperation among counties increases benefits (reduction of Vehicle Hours Traveled) for all participants in most cases. In some cases, cooperation also reduces protection costs. We have identified that there are counties that are not beneficial to protect, in terms of traffic delay minimization, such as Napa, while other counties for which protection is critical, such as Marin. However, these results depend on the formation of the collaborations and the available budget, and therefore our conclusions cannot be generalized.

Travel times are one of the most important outputs of transport planning models, especially in a strategic context. It is therefore paramount that the methods that underpin the construction of travel times are well understood. A plethora of methods exists to extract and/or construct travel times given some underlying network loading procedure, also known as the traffic flow propagation model. However, the relation between these different travel time methods and the consistency between such methods has received relatively little attention in the literature. This might in part be due to the many different traffic flow propagation models in existence, ranging from vehicle based (microscopic), to flow based (macroscopic), and models that explicitly account for the time varying nature of traffic flows (dynamic) to models that do not (static). In this work, we limit ourselves to flow based, i.e. macroscopic, traffic flow models. Within this modelling paradigm we consider dynamic, semi-dynamic, and static traffic flow propagation formulations used to construct link travel times. The semi-dynamic and static approaches are considered as more aggregate versions of the dynamic formulation. Within this context we formulate a unified (link) travel time formulation that is consistent across these three modelling paradigms under the assumption of steady-state flow conditions. The dynamic link travel time formulation is based on a recent state-of-the-art continuous time macroscopic dynamic network loading model. In the dynamic model we assume steady-state conditions to remain consistent with steady-state semi-dynamic and static approaches. This allows us to derive semi-dynamic link travel time formulations from our dynamic model, while the static formulation is derived from its semi-dynamic counterpart. Throughout this work we explore link travel times from three different perspectives; an experienced perspective, which actively tracks the tail of a physical queue, and two functional perspectives, both of which do not require explicit queue tracking. Based on the existing literature and proposed formulations, a classification framework is proposed allowing one to compare existing (and future) methods in the literature in an objective fashion. We provide a number of explicit derivations of existing model formulations that can be considered special cases of our unified approach. A numerical example across the different perspectives is included and a significant number of representative existing methods in the literature has been classified based on our proposed framework for the reader's convenience.

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.

Identifying traffic states at urban intersections plays a significant role in achieving the full potential of intelligent transportation systems for various traffic applications (e.g. real-time traffic signal control). Many traffic state identification methods use mean values of measured or estimated traffic flow variables to identify traffic states; however, lacking a consideration of traffic flow uncertainty leads to inaccurate identifications. To investigate the impact of traffic flow uncertainty on intersection traffic states, this article proposes an interval data-based k -means clustering method for traffic state identification at urban intersections. The uncertainty of three traffic flow variables (volume to capacity ratio, queue length, and delay) are represented in the form of interval data and employed as input variables. The proposed method was implemented on a real-world traffic network in Kunshan, China. Test results show that the clustering results are explicable and can accurately describe the trend of traffic state evolution. Further investigation shows that the proposed method outperforms the mean-value-based method, and queue length has a more significant contribution to the clustering results after the use of interval data. The findings of this study demonstrate the effectiveness of the proposed method in traffic states identification at urban intersections.

In an uncontrolled bus line, buses tend to bunch due to the stochastic nature of traffic flow and passenger demand at bus stops. Although bus holding has been shown to effectively prevent bus bunch...

With increasing number of vehicles registered on road the CO2 emissions and the amount of fuel wasted because of congestion has been rising. V2X and Connected and Automated Vehicle (CAV) technology allows vehicles and traffic infrastructure to communicate with each other, and could facilitate better use of existing resources by providing vehicles information about their surroundings and traffic signals. The information regarding the phase of traffic signal, vehicles’ position and vehicles’ speed can be used by drivers and autonomous vehicle control algorithms to make informed decisions as they approach traffic signals. This research proposes a coordination heuristic to improve traffic flow of CAV vehicles through traffic signals and reduce system wide emissions. A simulation was used to compare the system level impacts to emissions, travel time, and wait time of the proposed coordination heuristic to a single vehicle optimization strategy and baseline driver behavior. The results of this research suggest that traffic density and CAV market penetration impact the potential benefits of CAV control strategies. At low CAV penetration rates both the single vehicle and coordination heuristic produced increased emissions primarily driven by driving behavior of non-V2X enabled vehicles. At higher CAV penetration rates the coordination heuristic significantly outperformed the single vehicle control strategy and baseline driver behavior with regards to emissions reduction. The analysis indicates that at 900 vehicles per hour for either of the two driving strategies: coordination heuristic or single-vehicle optimization, to be more preferred over baseline driver behavior, at least 50% of the vehicles should be CAV. Once a threshold penetration rate of CAV vehicles is achieved, vehicles following the coordination heuristic generate nearly 10% fewer CO2 emissions than vehicles following baseline driver behavior, a 30% improvement over the reduction in CO2 emissions obtained using single-vehicle optimization. The vehicles following the coordination heuristic also have less travel time than vehicles following single-vehicle optimization, and less wait times than vehicles following baseline driver behavior.

No-car zone is becoming a trend in many parts of the world, mainly driven by environmental motives. Recent data has shown that both vehicle kilometers traveled and car ownership are on the decline, especially in developed countries. This has been coupled with the rise of new urban mobility concepts such as smart mobility, car sharing, and green modes. Can we push for these policies without compromising traffic circulation? The Braess Paradox (BP) suggests that road closure can, in fact, improve traffic congestion; dubbed as the Braess Paradox Detection Problem (BPDP). This study aims to leverage the BP to boost the idea of the no-car zone. To this end, we formulate the BPDP as a bilevel problem and develop a heuristic methodology (a surrogate-based algorithm) to identify roads for closure in the heart of the cities to be reclaimed for green space, pedestrian plazas, and so on. Furthermore, our proposed formulation considers travel demand elasticity resulting from road-closure schemes while solving for the BPDP. We test the methodology using a real dataset, the road network of Winnipeg. The results indicate strong evidence and justification supporting the idea of no-car zone even in the heart of highly congested cities.

Traffic prediction, as an important part of intelligent transportation systems, plays a critical role in traffic state monitoring. While many studies accomplished traffic forecasting task with deep learning models, there is still an open issue of exploiting spatial-temporal traffic state features for better prediction performance, and the model interpretability has not been taken serious. In this study, we propose a path based deep learning framework which can produce better traffic speed prediction at a city wide scale, furthermore, the model is both rational and interpretable in the context of urban transportation. Specifically, we divide the road network into critical paths, which is helpful to mine the traffic flow mechanism. Then, each critical path is modeled through the bidirectional long short-term memory neural network (Bi-LSTM NN), and multiple Bi-LSTM layers are stacked to incorporate temporal information. At the stage of traffic prediction, the spatial-temporal features captured from these processes are fed into a fully-connected layer. Finally, results for each path are ensembled for network-wise traffic speed prediction. In the empirical studies, we compare the proposed model with multiple benchmark methods. Under a series of prediction scenarios (i.e., different input and prediction horizons), the superior performance of the proposed framework is validated. Moreover, by analyzing feature from hidden-layer output, the study explains the physical meaning of the hidden feature and illustrate model’s interpretability.

Determining the effect automated and connected vehicles could have on traffic flow would – ideally – require testing the vehicles themselves in a real-world environment. In the absence of large-scale, real-world testing, researchers used traffic modeling software to develop and test a vehicle mimicking the behaviors of several automated and connected vehicle (CV) applications in a congested and complex urban network. The algorithm behind the CV ran a suite of mobility-focused applications, inspired by cooperative adaptive cruise control (CACC), speed harmonization, and queue warning applications. The CV was first tested on a small sample network, consistent with approaches obtained from a review of the literature. The research team then sought to understand the potential effects of CV technology on congestion and mobility in a DTATexas context by modeling the traffic impacts of CVs at varying market penetrations on a twelve-mile section of I-35 in Austin, running from south of Riverside Dr. to Parmer Ln at 2035 population levels. Researchers used a multi-resolution modeling (MRM) methodology, which incorporates macroscopic, mesoscopic, and microscopic models. Keywords: Connected vehicles, variable speed limit, Cooperative adaptive cruise control, Queue warning, Multi-resolution modeling

In 2015, the Ecuadorian regulation related to noise considered the need to develop noise maps as a strategic tool for the noise pollution management and urban planning. However, no specific methodology has been established yet. Therefore, this study aims to develop Quitos first traffic noise map by using computer-aided predictive model, an official data of road network, buildings and population. Three noise prediction models (RLS-90, STL-86, and NMPB-Routes-08) were initially used to compare the modelled and measured results. Short time noise measurements and vehicle counts were used as a means of prediction model validation. The comparison showed that the RLS-90 model was the most suitable for predicting traffic noise levels, in this case of study. Using simplified information about traffic flow, building sound absorption and road surface, a cost-effective traffic noise assessment was obtained. The results of noise evaluation on building facade estimates that during daytime 25% of the people living in the urban area of Quito is exposed to road traffic noise levels above 65 dBA, considered as high and undesirable. While the results for night-time estimate that 38% of the population are exposed to traffic noise levels, considered as increasingly dangerous for public health. The results of this study may be considered as a starting point for the city urban planning, mobility managing and policy making.

The advancement of vehicular technologies for automated driving will lead to a mixed traffic flow that depends on the interaction between automated vehicles (AVs) and manually driven vehicles (MVs) because the market penetration rate (MPR) of AVs will gradually increase over time. Because automated driving environments provide us with a valuable opportunity for controlling individual vehicle operation, a strategic policy for managing AVs operation is expected to enhance the performance of the traffic stream. Therefore, the operation of AVs needs to be properly determined to cope with various traffic and road conditions and thereby facilitate smooth and effective vehicle interactions. This study proposed a novel traffic management strategy in automated driving environments by adjusting the driving aggressiveness of AV operation, defined as automated driving aggressiveness (AuDA). VISSIM microscopic simulation experiments were conducted to derive the proper AuDAs to enhance both the traffic safety and the mobility performance. Traffic conflict rates and average travel speeds were used as indicators for the performance of safety and operations. While conducting the simulations, the level of service (LOS) and MPR of the AVs were also considered. In addition, the relationship between key variables for adaptive cruise control (ACC) operations and AuDA policies was explored to better support the understanding of how the proposed methodology works in practice. Promising results showed that the proposed methodology would be effective in optimizing the performance of mixed traffic conditions. Furthermore, the outcome will be valuable in developing various policies and guidelines to manage the operation of AV in automated driving environments.

Short-term traffic flow forecasting is a fundamental and challenging task since it is required for the successful deployment of intelligent transportation systems and the traffic flow is dramatically changing through time. This study presents a novel hybrid dual Kalman filter (H-KF 2 ) for accurate and timely short-term traffic flow forecasting. To achieve this, the H-KF 2 first models the propagation of the discrepancy between the predictions of the traditional Kalman filter and the random walk model. By estimating the a posteriori state of the prediction errors of both models, the calibrated discrepancy is exploited to compensate the preliminary predictions. The H-KF 2 works with competitive time and space to traditional Kalman filter. Four real-world datasets and various experiments are employed to evaluate the authors’ model. The experimental results demonstrate the H-KF 2 outperforms the state-of-the-art parametric and non-parametric models.

Traffic outlier detection is an essential topic in city management and data mining. Most traffic outliers are caused by accidents, control, protests, disasters, and many other events. Recently, traffic outlier detection methods are based on counting traffic flow, where the detection is inherited from the periodical changes of road flow. However, these approaches fail to detect the impact from upstream roads. Furthermore, the mappings between traffic congestion level and traffic flow are distinct from road to road. In this study, a Poisson mixture model (PMM) – coupled hidden Markov model (CHMM) outlier detection method would be introduced for detecting traffic anomalies which are from taxi global positioning system data. To make a finer-grained outlier detection method, road traffic congestion estimation, as well as the impact from upstream roads, are considered. PMM serves as an estimator to determine the congestion level for every road and CHMM is used to couple the road's impact. The experiment applies both semi-synthetic and real outlier data from the Beijing map, and the results reveal the advantages of both datasets.

In view of the advantages and a promising market prospect of the emerging connected automated vehicles (CAVs), it will be very likely that the roadway is shared by CAVs and RHVs in the near future....

Cities are the main source of CO2. Traffic is one of the most important sources of CO2 emissions in urban areas. One-year CO2 flux and 36-days traffic flow data were collected by field monitoring in 2014 at the study area. The accurate traffic CO2 emissions on 30-min scale were calculated by the localized MOVES model. The annual emissions were 4139.19 Mg. The traffic CO2 emissions showed a “double-peak” diurnal pattern, with morning peak of 705.14 kg 30 min−1 (8:00–8:30) and the evening peak of 788.40 kg/30 min−1 (7:30–18:00). The tail number limited measure in Beijing has reached certain extent, with emissions by limited tail number as follows: 1 or 6 (11.03 Mg/d) radiation (−0.152) > traffic CO2 emissions (0.107); for the plant growing season, radiation (−0.254) > temperature (−0.130) > traffic CO2 emissions (0.105).

Model-based optimal control strategies were capable of handling saturated traffic regimes with the ability to maintain optimised network performance. However, the upstream queue spillback which was not considered by these strategies renders an inefficient handling of oversaturated traffic regime. Hence, oversaturated situations needs both traffic flow coordination and queue spillback regulation. Here, the author aims to regulate oversaturated regimes with model-based control strategy (MCS) based on the Lighthill-Whitham-Richards–integrated model (LWR-IM) traffic model. A traffic simulator which was developed in a previous work is further extended with the proposed MCS. The MCS comprised of a rolling horizon network prediction and control algorithms at both the network and intersection levels. Based on future predictions of platoon sizes, arrival times, and queues; phase durations are adjusted at the intersection level by employing a proportionate methodology in responding to the incoming platoons and a regulation strategy to handle queue spillback. The proposed MCS was tested on real-world traffic via simulations to assess its performance compared to existing fixed-time system. The MCS improved the average delays and network throughput significantly and manage to keep queues from spilling back upstream. In addition, the strategy is feasible for real-time implementation.

Traffic flow estimation (TFE) is an important task for intelligent transportation systems, especially for expressway system in travel demand management, road conditions monitor etc. Traffic flow is usually detected by fixed sensors, which is neither practical nor economically feasible to cover all expressway component links. Additionally, the existing research usually ignores the effects of drivers’ dwell behaviours on TFE. To fill these gaps, this study proposes a TFE method based on toll ticket data (TTD) which records trip information, except the actual route. Meanwhile, dwell time, as a key component of drivers’ journeys, will be integrated into the proposed approach. In this study, a Gaussian mixture model for the origin–destination pairs with multiple alternative routes will be formulated. Moreover, expectation–maximisation algorithm is introduced to estimate link traffic flows and dwell time distributions simultaneously. By TTD collected from the Shandong Province Expressway in China, the proposed approach is calibrated and applied to TFE empirically. The results show that the proposed approach has a good performance on TFE with consideration of dwell time.

Recently, an open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. Aiming to bridge the modeling gaps between multi-agent simulation and optimization in a multimodal environment, we further design and develop DTALite-S to simplify the traffic flow dynamic representation details in DTALite for future extensions. We hope to offer a unified modeling framework with inherently consistent space–time network representations for both optimization formulation and simulation process. This paper includes three major modeling components: (1) mathematic formulations to describe traffic and public transportation simulation problem on a space–time network; (2) transportation transition dynamics involving multiple agents in the optimization process; (3) an alternating direction method of multipliers (ADMM)-based modeling structure to link different features between multi-agent simulation and optimization used in transportation. This unified framework can be embedded in a Lagrangian relaxation method and a time-oriented sequential simulation procedure to handle many general applications. We carried out a case study by using this unified framework to simulate the passenger traveling process in Beijing subway network which contains 18 urban rail transit lines, 343 stations, and 52 transfer stations. Via the ADMM-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status.

The traffic weaving area is one of the key bottlenecks affecting the operation efficiency of an urban expressway system. The main motivation of this study is to explore the effects of lane allocation on the operational efficiency at weaving areas. A cellular automaton model was established, in which three different lane-changing rules were considered to match the driving behaviour when the lanes are allocated. Using the average vehicular travel time and the average velocity as the indicators, the operational performance of various lane allocation schemes was compared under different traffic and geometric conditions. A case study is used to validate and evaluate the effectiveness of the proposed method. The results show that the benefits of the lane-allocation strategy are affected by the traffic flow, weaving volume ratio, weaving length, and installation location of the isolation facilities. The lane-allocation strategy has promising application under the conditions of a traffic flow that is >400 veh/ln, at weaving volume ratio between 0.2 and 0.7, and weaving length shorter than 300 m. Moreover, the isolation facilities should cover the weaving area when the lane-allocation strategy is used.

Previous studies have attributed the relatively high risk of elderly pedestrians to their declining abilities, particularly the ability to estimate their own walking time and time-to-arrival of oncoming vehicles. However, how these two abilities affect street-crossing safety in mixed traffic conditions remains unclear. This study conducted a simulation study using filmed real-life traffic to investigate this issue. Repeated measures of gap-selection decision and safety outcomes among 82 adults (42 elderly participants aged 60 years or older and 40 aged between 20 and 45 years) were collected. Two sets of scenarios were developed: one car–one lane simple-flow tasks and multiple vehicle–one lane complex-flow tasks. The analysis results demonstrated that in simple- and complex-flow tasks these two abilities are both critical to gap-selection safety; however, pedestrians’ reliance on walking-time projection is more consistent than is their reliance on TTA projection when making gap-selection decisions. Although young pedestrians are safer on average than elderly pedestrians, how they respond to oncoming vehicles is similar; in particular, both young and elderly pedestrians tend to select longer gaps even when oncoming vehicles are moving quickly. The responses of the elderly pedestrians were relatively varied and skewed toward being unsafe, and thus their on-average gap-selection performance was inferior to that of the young pedestrians. The benefit of offering countdowns to oncoming vehicle arrivals, as a means of enhancing gap-selection safety, is appropriate when traffic flow is simple but relatively less so in complex-flow scenarios, indicating the importance of considering information overload in countermeasure design.

This paper elucidates the impacts of vehicle heterogeneity on traffic dynamics and throughput of mixed traffic consisting of connected automated vehicles (CAVs) and regular vehicles (RVs). The main premise is that the heterogeneity in preferred acceleration rate, desired speed, and car-following (CF) behavior (e.g., reaction pattern and sensitivity to a traffic disturbance) will change traffic properties in ways that can undermine traffic flow throughput. This paper first decomposes the mechanism into two elements – one driven by acceleration and one by time-varying CF response to disturbances – and then investigates their compounded effect. This paper also provides unifying frameworks to analyze the behavior of RVs and CAVs to facilitate analytical investigations. The results reveal how heterogeneous acceleration and CF behavior may create persistent voids and diminish traffic throughput. Integrating all the elements, throughput reduction is quantified via numerical simulations.

The primary focus of this paper is to govern traffic congestion on urban road networks based upon a cumulative approach comprising of traffic flow modeling, vehicle emission modeling, and air quality modeling. Based upon the traffic conditions, a simulation model is proposed and further tested for performance metrics, which is relative to three main aspects, namely, the waiting time of the vehicles at the junctions/intersections/signals, the type of pollutant emitted by a vehicle, and the traveling time. The experimental analysis and validation are carried out for different case studies in Malaysia, such as Petaling Jaya, Shah Alam, Mont Kiara, and Jalan Tun Razak. Three different scenarios (morning, afternoon, and evening) are analyzed and tested to explore the traffic usage parameter. The results showed that when traffic is modeled and governed based upon traffic flow, vehicle emission, and air quality index (AQI), nearly 75% of traffic congestion is mitigated, hence making the atmosphere pollution free as well as avoiding Urban Heat Island (UHI) effect due to the heat generated from vehicles. The experimental results are tested, validated, and compared with existing solutions for performance analysis. The proposed model is aimed toward overcoming the major drawbacks of existing approaches, such as single-path suggestions, traffic delay during peak hours/emergencies, non-recurring congestion consideration, congestion avoidance instead of recovering from it, improper reporting of road accidents, and notifications about traffic jam ahead to the users and high vehicle usage rate.

In this paper, we investigate the performance of a bus lane that is (de)activated under specific road traffic and public transport conditions. More specific, the VISSIM microscopic simulation software along with VisVAP and EnViVer add-on modules were employed to represent the operation of a bus lane with intermittent use by cars in a highly congested arterial. We modelled various scenarios to determine the level of traffic volumes and bus service frequencies for which such bus lane concept would be effective. Empirical findings showed that when peak hour volumes range between 1000 and 2000 vehicles, the mixing of cars and buses on bus lanes may lead to a) serious reductions of vehicles’ queue lengths, b) significant decreases of greenhouse gas and air pollutant emissions derived from traffic flow and speed profile improvements and c) financial viability of the experiment. The joint use of bus lanes at 2000 vehicles per peak hour was highlighted as the most financially viable scenario. On the other hand, delays were comparatively increased due to the lower speed of cars which were moving onto the bus lane. Finally, our bus lane concept was not performed well under bus headways shorter than 2 min.

Future Air Traffic Management systems can benefit from innovative approaches that leverage the increasing availability of operational data to facilitate the development of new performance assessment and decision-support capabilities. This paper presents a data analytics framework for high-fidelity characterization of air traffic flows from large-scale flight tracking data. Machine learning methods are used to exploit spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a comparative analysis of terminal area operations in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the multi-airport systems analyzed. Our findings show that New York presents the most complex airspace design, with considerably higher number of routes and interactions between them, as well as more dynamic changes in the terminal area flow structure during the day, in part driven by the presence of flow dependencies. Interestingly, it exhibits the best levels of traffic flow efficiency on average, both spatially and temporally, yet the highest variability in metroplex configuration performance, with more pronounced performance degradation during inclement weather.

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.

This study is an attempt to establish a suitable speed–density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all static and dynamic properties of speed–flow–density relationships. As a first attempt for Indian traffic condition, two behavioural parameters, namely the kinematic wave speed at jam (C j) and a proposed saturation flow (λ), are estimated using empirical observations. The parameter C j is estimated by developing a relationship between driver reaction time and vehicle position in the queue at the signalised intersection. Functional parameters are estimated using Levenberg–Marquardt algorithm implemented in the R statistical software. Numerical measures such as root mean squared error, average relative error and cumulative residual plots are used for assessing models fitness. We set out several static and dynamic properties of the flow–speed–density relationships to evaluate the models, and these properties equally hold good for both homogenous and heterogeneous traffic states. From the numerical analysis, it is found that very few models replicate empirical speed–density data traffic behaviour. However, none of the existing functional forms satisfy all the properties. To overcome the shortcomings, we proposed two new speed–density functional forms. The uniqueness of these models is that they satisfy both numerical accuracy and the properties of fundamental diagram. These new forms would certainly improve the modelling accuracy, especially in dynamic traffic studies when coupling with dynamic speed equations.

Traffic congestion has been a major problem in big cities around the world, not to mention several large cities in Indonesia. Bandung is the second largest metropolitan area after Jakarta in Indonesia which suffers from extreme levels of congestion. With a high number of motorcycles and large private car population, congestion in this city is ever growing worsening the environment. While the local authorities struggle to find resources to fund capital intensive capacity expansion projects, this research explores the use of cost effective demand management policy measures to reduce the congestion and pollution. This study aims at assessing two relatively under-researched demand management policy measures that restrict vehicle flows viz., car-free day and odd-even plate schemes to investigate the effect on traffic congestion and the environment. SATURN traffic network modelling software has been used to predict the route choices of vehicles. Bandung city road network and origin destination matrix have been adapted to simulate the two measures during the peak hour. As well as providing the necessary inputs to a pollutant emission estimation model, traffic network modelling output forms the basis for assessing the congestion levels. Results show that both car-free day and odd-even plate measures have unintended consequences that undermine their effectiveness which if addressed could make them highly beneficial solutions. Car-free day scheme reduces the traffic flow levels in the vicinity of scheme but diverts the vehicle flow elsewhere to other routes which may adversely affect the congestion/pollution. Odd-even plate scheme is very effective at the beginning of its implementation but the performance gradually diminishes as drivers start to adapt by buying a second vehicle or even using fake number plates.

The autonomous vehicle innovation has a potential effect on traffic and it’s a widespread issue in the academic municipal. Based on its feature, autonomous vehicle is expected to improve traffic flow, reduce accidents, reduce social exclusion and improve the utility of time on travel. It is increasingly accepted that the next step to the evolution of human transportation is the replacement of human as the driver by the Artificial-intelligence-capable machine. Regardless, the challenges remain especially in convincing the consumers to switch to autonomous cars despite the apparent benefits. In the past, papers highlighted individual trust toward machine as the key point to encourage acceptance of autonomous cars. This paper seeks to highlight the ethical implications of the autonomous technology and how it is related to the user acceptance of the technology. This paper reviewed recent studies on the ethical implications of the autonomous vehicle and the user acceptance of the technology. A systematic review of papers from the literature was done using Scopus, ISI and Web of Science. The results of the review have shown that the level of trust, which may vary on the sociodemographic profile of the users, has been studied as one of the factors for user acceptance. Many researchers also highlight ethical implication as an important aspect of autonomous technology that needs to be tended to. The researcher proposes that ethical implication can be an important element of user trust toward autonomous technology. Hence, there is a need to embed ethical implication as a construct for user trust leading to user acceptance model in future studies. In the line of this research review researcher was addressed the following comprehensive groups: examination of autonomous vehicle topographies and flexibility prototypes, consequences for street traffic and connectivity to substructure (specifically in the urban areas with the medium and low density), communal attitudes and apprehensions, transportable behaviour and plea, possible business models, and strategy implications. Lastly, the main objective of this research article is to recognise critical problems for the growth of an effort cluster investigation to comprehend attitudes of possible manipulators of AVs and to highpoint autonomous vehicle growth issues for Malaysian lawful agencies.

The cell transmission traffic flow model (CTM) has attracted considerable interest in the field of transportation due to its simplicity as well as the ability to capture most of the macroscopic traffic flow characteristics. The stability properties of the CTM under different demand and capacity constraints are not always obvious. In addition, the impact of microscopic phenomena such as forced lane changes at bottlenecks leading to capacity drop is not captured by the CTM. In this paper, we start with a single section and modify the CTM to account for capacity drop. We analyze the stability properties of the CTM under all possible demand and capacity constraints as well as all possible initial density conditions. The analysis is used to motivate the design of variable speed limit (VSL) control to overcome capacity drop and achieve the maximum possible flow under all feasible traffic situations. The results are extended to multiple sections, where the stability properties of the open-loop system are analyzed and a VSL control scheme is designed and shown to achieve the objective of maximizing the traffic flow under different demand and capacity constraints. Unlike the open loop system where an infinite number of equilibrium points exist under certain demand levels, the proposed nonlinear VSL scheme guarantees exponential convergence to a unique equilibrium point that corresponds to maximum possible flow and speed under all possible demand levels and capacity constraints.

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior. On the other hand, an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks. This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective optimizer for short-term prediction. NSGA-II is used to optimize the number of neurons in the first and second layers of the NN, learning ratio and slope of the activation function. This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way. Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway. Results are analyzed based on the performance measures, showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment. The achieved prediction accuracy is calculated with multiple measures such as the root mean square error (RMSE), and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction, respectively.

Modern roundabouts are widely used at intersections with light traffic, generally providing safety and other advantages. However, large entry delays are often observed at roundabouts with unbalanced flow patterns, even though the entry traffic flow is not high. A metering signal-based strategy is examined to mitigate the above problems. A mathematical optimisation model is formulated firstly with the objective of minimising the total entry delay, subject to the metering signal thresholds. Then a solution algorithm based on VISSIM simulation is developed. Finally, a case study is carried out to testify the feasibility and applicability of the proposed model. Extended scenarios analyses under different levels of approach volume, different demand combinations and different proportions of right-turn vehicles (left-side driving) are also conducted. Results show that the methodology can effectively improve the operational performance, and a delay reduction of up to 25.7% can be expected using the metering signal-based strategy. This can provide a criterion for the use of metering at the roundabout.

Hong Kong Society for Transportation Studies Limited. The development in communication technologies offers efficient vehicular communication capability. Vehicles in communication with road infrastructure share essential traffic information that can be used to develop cooperative driving strategies. In this paper, we aim to propose a heuristic and cooperative method to improve efficiency and merging capacity for highway T-junctions. We evaluated a few performance indicators including travel time, safety and emission. Results indicate that a significant improvement is observed in comparison with the baseline benchmarking case. We further conducted simulations with different geometric configurations and pointed out a few issues that need to be addressed in order to maximise the benefit.

Vehicle delay and traffic emissions have serious influences on traffic flow operations on an arterial street. The emission factors during green and red are calibrated based on vehicle-specific power (VSP), then three optimisation models are formulated by minimising vehicle delay or traffic emissions and by considering left-turn bays and coordinated signals. The field data from Dalian city of China are used to validate these models. Vehicle compositions include car, medium-size vehicle (MSV) and bus. Carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NO x ) emissions are estimated. Three coordinated signal control scenarios are proposed and four simulation experiments are carried out. The results indicate that the emission factor during green is greater than that during red for CO, HC or NO x produced by car, MSV or bus in each lane group on an arterial street; the emissions estimated by the motor vehicle emission simulator model are greater than those calculated by the VSP-based model; and the signal control scenario obtained by minimising total emissions performs better than that obtained by minimising total delay or both of them.

Bus services can be seen as a way to reduce traffic congestion where they can encourage a mode shift from car. However, they can also generate negative effects on traffic flow due to stop-start operations at bus stops. This paper aims to assess the net impact of bus operations on traffic congestion in Melbourne. The methodology used to achieve this aim comprised of three main stages. First, a primary survey was conducted to determine the mode shift from bus to car when buses are unavailable. This figure was used to estimate the positive impact of buses on relieving congestion. Second, the negative impact of buses was investigated by considering the effect of bus stop operations on vehicle traffic flow using microsimulation. Finally, the net effect was estimated by contrasting congestion measures determined from a traditional four step model between two scenarios: ‘with bus’ and ‘without bus’. The results indicated that Melbourne’s bus network contributes to reduce the number of severely congested road links by approximately 10% and total delay on the road network by around 3%. The highest congestion relief impact was found in inner Melbourne with a 7% decrease in vehicle time travelled and total delay, and 16% decrease in the number of heavily congested road links. In inner areas, the level of congestion is relatively high so the mode shift from car to bus, even if not as high as middle and outer areas, have a significant effect on relieving traffic congestion. Areas for future research are suggested such as investigating the long-term effect of buses on traffic congestion.

In this paper, we seek to investigate the spatiotemporal impacts of traffic incident on urban road networks. The theoretical lens of a complex network leads us to expect that incident impacts are associated with the functionality that an intersection acts in a network, and also, the location of incident sites. Incident impacts are measured in both temporal and spatial dimension through mining the large-scale traffic flow data in conjunction with the incident record. In the complex network context, the urban road network can be converted into a weighted direct graph with intersections as nodes and road segments as edges with their geographic information. Four network features, i.e., Betweenness Centrality, weighted PageRank, Hub, and K-shell are assigned to each intersection to measure its functionality. Temporally, we find out significant correlations between incident delay and two network features by applying hazard-based models. Spatially, the micro impact and the macro impact are found to be strongly associated with three network features through estimating a Bayesian Negative-binomial Conditional Autoregressive model and a generalized linear model, respectively. Our study provides the basis of leveraging urban road network context to evaluate incident impacts, with some explanations, insights and possible extensions that would assist traffic administrations to guide the post-incident resilience and emergency management.