Traffic Operation Characteristics Research Articles

Analyzing Freeway Safety Influencing Factors Using the CatBoost Model and Interpretable Machine-Learning Framework, SHAP

Exploring and analyzing safety influencing factors can guide targeted traffic safety management. Traditional traffic safety models are aimed at specific data problems and making adjustments to the model structure, which lack focus on predictive ability and have limited information on the analysis of influencing factors. In recent years, machine-learning methods have opened new avenues in modeling that have higher prediction accuracy, can identify complex nonlinear relationships, and can overcome over- and under-dispersion and correlation. Machine-learning methods, however, pose the problem of limited interpretability. The interpretable machine-learning framework SHAP can be an effective solution, which can not only reflect the influence of features in each sample but also generate global interpretation. This study established gradient boosting models including the CatBoost and XGBoost models as traffic safety models, which were compared with a traditional NB regression model and a zero-inflated negative binomial regression model. SHAP was used to analyze several safety influencing factors, including geometric design features, traffic operation characteristics, time of day, and land use. Results confirmed that the CatBoost model has better prediction ability and is a more suitable traffic safety model than the traditional negative binomial regression model. Among the key findings were that ramp type is the most important factor in freeway crash frequency; curve presence has a great positive impact, while truck proportion has a great negative impact; and traffic volume is highly correlated with truck proportion. These findings can provide theoretical support for safety operation management and targeted improvement measures for freeways.

Traffic Performance Measures on Two-Way Two-Lane Rural Highways

The most appropriate service measure for a two-lane highway is the combination of average travel speed (ATS) and percent time spent following (PTSF). The local driver behavior and traffic operation characteristics in different countries affect the operational analysis results. The objective of this study is to evaluate the applicability of the HCM procedures on two-lane highways in Iraq. Ten highway segments in northern Iraq were selected, and more than 28,000 vehicles were observed to record traffic characteristics.
 The results show that for low flow rates, the HCM-2000 procedures tend to overestimate the ATS with an amount of 0.0085VP, while for moderate-to-high flow rates, the HCM procedures tend to underestimate the ATS with an amount of 0.0025VP for the highways considered. Moreover, the HCM procedures tend to overestimate PTSF; as well, ATS decreases as PTSF, density, and follower density increase for the highways under study. Equations are developed for the evaluation of traffic performance on two-lane highways in Iraq.

Optimization Design and Performance Evaluation of U-Shaped Area Operation Procedures in Complex Apron

In view of the common U-shaped apron structure of large- and medium-sized airports at home and abroad, this study considered the optimization design and performance evaluation of the U-shaped apron operation procedure. First, by analyzing the physical structure characteristics and traffic operation characteristics of the U-shaped area, exclusive, partition-shared, and global-shared operation procedures of the U-shaped area were designed, and differentiated apron-operation rules and traffic models were constructed for different types of operation procedures. Then, from the perspectives of safety, efficiency, and environmental protection, a multi-dimensional evaluation index system of U-shaped area operation performance is established, and a classification measurement and comprehensive evaluation method based on critique is proposed. Finally, a traffic simulation model was established based on airport network topology modeling. We used Monte Carlo methods for the simulation in Python 3.6, and the experimental results show that, in the scenario of high-density traffic operation, compared with exclusive and partition-shared procedures, the implementation effect of the global shared procedure is very significant, and the apron operation capacity increased by 14.8% and 5.0%, respectively. The probability of aircraft conflict decreased by 32.2% and 11.8%, respectively, and the time of single conflict relief decreased by 16.1 s and 10.6 s, respectively. The average resource utilization in each U-shaped area increased by 66% and 25%, respectively, while the average daily carbon emissions of a single aircraft were reduced by 16.7 kg and 11.0 kg and the average daily fuel consumption of a single aircraft were reduced by 3.6 kg and 2.4 kg, respectively. The proposed method is scientific and effective and can provide theoretical and methodological support for optimizing the configuration of the scene operation mode of complex airports and for improving flight operation efficiency.

A conflict-based safety assessment technique for rear-end crash risk at signalized intersections in a lower-middle-income country: A comparison between homogeneous and heterogeneous traffic conditions

Rear-end crashes at signalized intersections are attributed to different traffic behavior and operational characteristics. However, the influences of homogenous traffic conditions in developed countries and heterogeneous traffic conditions in low and middle-income developing countries like India are rarely explored. This study aims to examine the spatial–temporal characteristics of rear-end crash risk at signalized intersections characterized by lane-disciplined homogeneous and non-lane-based heterogeneous traffic conditions. In particular, this study has applied a conflict-based safety assessment framework to compare the rear-end crash risks across these traffic conditions. Vehicular trajectory data of vehicles at four signalized intersections, one in the homogeneous and three in heterogeneous traffic conditions, is analyzed. Rear-end conflicts position and time of occurrence in the upstream and downstream section of the stop-line are identified and modeled by extreme value models. Results show that the vehicles in heterogeneous traffic conditions are more aggressive, resulting in a higher number of conflicts and probable crashes. The presence of smaller-sized vehicles in dominating proportions and disregard of lane discipline with a higher degree of lateral movement are responsible for more traffic conflicts. The estimated extreme value models suggest that the minimum rear-end crash risk probability at signalized intersections with heterogeneous traffic conditions is about three times higher than that at signalized intersections with homogeneous traffic conditions. The observation is similar for crash risks estimated from both surrogate safety measures used in this study. The weak lane discipline and irregular lateral movement of vehicles are responsible for higher crash risks in heterogeneous traffic conditions.

Analysis of Traffic Operation Characteristics and Calculation Model of the Length of the Connecting Section between Ramp and Intersection

Many cities have built expressways to alleviate traffic congestion, among which elevated expressways are the most common form. However, traffic congestion still occurs frequently in the connecting section between the ramp of expressway and the ground intersection. Based on the field traffic survey data, the traffic operation characteristics of vehicles in the connecting section and the main factors affecting the length of the connecting section are analyzed. A combined model for calculating the length of the connecting section between ramp of urban expressway and intersection is proposed. VISSIM is used to simulate the traffic flow under the current and calculated length of the connecting section. The comparison results show that under the calculation length, the travel time, average delay, parking time and queue length are reduced to varying degrees, which verifies the rationality of the calculation model.

An Improved Cellular Automaton Traffic Model Based on STCA Model Considering Variable Direction Lanes in I-VICS

In this paper, we propose an improved cellular automaton model for the traffic operation characteristics of variable direction lanes in an Intelligent Vehicle Infrastructure Cooperation System (I-VICS). According to the proposed flow of variable oriented lane operation in the I-VICS environment, the idea for the improved model has been determined. According to an analysis of different signal states, an improved STCA model is proposed, in combination with the speed induction method of I-VICS and the variable direction lane switching strategy. In the assumed regular simulation environment, the STCA and STCA-V models are simulated under different vehicular densities. The results indicated that traffic parameters such as traffic flow and average speed of the variable direction lanes in the I-VICS environment are better than those in the conventional environment according to the operating rules of the proposed model. Moreover, lane utilization increased for the same density.

A Novel Approach to Calculate the Spatial-Temporal Correlation for Traffic Flow Based on the Structure of Urban Road Networks and Traffic Dynamic Theory.

Determining the spatial–temporal correlation (STC) between roads can help clarify the operation characteristics of road traffic. Moreover, this correlation affects the utilization quality of traffic data in related research fields. Therefore, it is of significance to provide more reasonable correlation information for other research, such as in traffic speed prediction. Most of the traditional correlation calculation methods for traffic are based on only statistical theory. These methods are simple, but their ability to explain the actual phenomenon is limited due to the lack of consideration of the actual traffic operation characteristics. Therefore, to provide more reasonable correlation information between roads, this paper analysed the influence mechanism of urban traffic based on the traffic dynamic model, and two parameters, traffic complete influence time and traffic correlation strength, were proposed to bring physical meaning to the calculation of STC. Then, an improved calculation model of the STC between different roads considering the adjacency between roads was proposed in this paper. Finally, this paper verified this method against two common traditional methods through different experiments. The verification results show that the calculation method proposed in this paper has better interpretability for the STC between different roads and can better reveal the internal traffic operation characteristics of the road network.

Trajectory data based freeway high-risk events prediction and its influencing factors analyses

The frequent crash occurrences have caused massive loss of lives and properties all over the world. In order to improve traffic safety, it is vital to understand the relationships between traffic operation conditions and crash risk, and further implement safety countermeasures. Emerging studies have conducted the crash risk analyses using discrete and aggregated traffic data (e.g., loop detector data, probe vehicle data), where crash events were selected as the prediction target. However, traditional traffic sensing data obtained at segment level cannot describe the detailed operation conditions for the vehicle platoons near crash locations. Thus, more microscopic and high-resolution traffic sensing data are needed. In addition, considering the random occurrence feature of crashes, high-risk events should be paid more attentions given their higher occurrence probability and consistent causations with crashes, which could proactively reduce crash likelihood. In this study, HighD Dataset from German highways was utilized for the empirical analyses. First, high-risk events were obtained using safety surrogate measures with Modified Time to Collision (MTTC) less than 2 s. Traffic operation characteristics within 5 s prior to event occurrence were extracted based on vehicle trajectory data. Then, a total of three different logistic regression models were established, which are standard logistic regression model, random-effects logistic regression (RELR) model, and random-parameter logistic regression (RPLR) model. Among which, the RPLR model was showed to have the best fitness and prediction accuracy. The results showed that the disturbed traffic flows in both longitudinal and lateral directions have positive impacts on high-risk events occurrence. Besides, too close following distance between vehicles would lead to high-risk events. Moreover, RPLR models could provide a high prediction accuracy of 97 % for 2 s ahead of the high-risk events. Finally, potential safety improvement countermeasures and future application scenarios were also discussed.

Traffic Status Dynamic Evaluation of an Eight-Lane Highway Based on the Lane Level

Traffic status analysis is the basis for highway management and control. Most of the previous research studies were carried out from the perspective of road segments. The purpose of this paper is to analyze the difference in traffic operation characteristics of an eight-lane highway from the perspective of lanes. Lane saturation, average lane speed, and lane density were selected as traffic state evaluation indicators, and the concept of state space division was introduced to classify the traffic operation status of each lane. Then, a single-system multi-index dynamic comprehensive evaluation method was adopted to reflect the time-varying characteristics and the development trend of the operation status of each lane. The research results can provide the theoretical support for more refined highway management and improve the service level of the eight-lane highway.

Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System.

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

Status of signalized intersection safety-A case study of Kolkata

Urban signalized intersections in Indian cities experience a high share of fatalities and serious injuries. However, assessment of safety using rigorous statistical technique is still in an early stage in India, primarily due to challenges in obtaining crash data, road geometric and traffic data. This study intended to focus on safety issues associated with the planning, design, and operation of signalized intersections, and identify the risk factors affecting the frequency and severity of crashes. Assuming that user behavior is influenced by the design and traffic operational characteristics of a location, this study specifically aims to investigate the effects of geometric, infrastructural, traffic control and land use parameters on the number of crashes and their severity across 52 signalized intersections of Kolkata. These data were associated with crash records from 2011 through 2014, to extract important insights, using statistical tests such as correlation analysis and independent samples t-test, as well as multivariate regression analysis for crash prediction purposes. Based on the assessments carried out in this study, there is evidence that factors such as total approach traffic volume, blocked carriageways, traffic configuration, presence of protected right turning phase, type of minor road, tram stops, etc. significantly affect the frequency of crashes while presence of all-red time, protected right turning phase, blocked carriageway, non-motorized traffic, and visibility of road markings affect the severity of crashes at signalized intersections of Kolkata. In the absence of other road traffic safety studies involving rigorous statistical analysis, these findings can significantly contribute to policy-making decisions that are aimed at improving the safety of signalized intersections in the city of Kolkata.

Coordinated Intersection Signal Design for Mixed Traffic Flow of Human-Driven and Connected and Autonomous Vehicles

This manuscript investigated coordinated intersection signal design problem for mixed traffic flow of Human-Driven Vehicles (HDVs) and Connected and Autonomous Vehicles (CAVs). Two main macroscopic impact of the mixed flow on signal setting are considered: saturation flow rate and the platoon dispersion. In order to capture the traffic flow operational characteristics on coordinated intersections, three locations, namely entrance location where the loop detector was located at, and upstream intersection and downstream intersection were defined. Two types of vehicle cumulative curves, namely cumulative arrival profile and cumulative departure profile were constructed. The mixed-flow traffic dynamics were analyzed, and the arrival-departure curves relationship was derived using a combination of Newell car-following and Akcelik acceleration model. A mixed-flow platoon dispersion model was proposed to describe the vehicle’s progression between two locations. Due to the nonlinear nature of the problem, a particle swarm optimization (PSO) method was employed to obtain the optimal signal parameters, including the cycle length, green duration, and optimal offset. The algorithm was implemented and validated in a case study involving two intersections, with the demand formulated and simulated by the Markov chain. The results showed that the proposed model could effectively decrease delays when compared with current signal control methods.

Impact of Road Infrastructure Land Use and Traffic Operational Characteristics on Pedestrian Fatality Risk: A Case Study of Kolkata, India

Pedestrian safety in urban India is an issue of growing concern. While an extensive body of literature is available on the identification of elements related to the pedestrian’s risk, the studies are mainly conducted in urban areas of developed countries, where the condition of infrastructure, built environment, road user’s behavior and perception are very different from Indian cities. As a result, findings from these studies may only be partially applicable to the cities of developing countries such as India. The present study makes an attempt to fill the gap by analyzing historical crash records obtained from Kolkata police and identifies the hazardous corridors and network locations posing the highest risk to the pedestrian. Subsequently, the binary logistic regression models are developed to identify the risk factors associated with road infrastructure, land use, and traffic operational characteristics. Based on the statistical assessment carried out in this study, there is evidence that factors such as road width, land use type, the absence of designated bus stop facility, inadequate sight distance, average daily traffic and pedestrian volume, and pedestrian–vehicular interaction considerably affect pedestrian safety at the intersections. On the other hand, road width, land use type, inadequate sight distance, the absence of designated bus stop facilities significantly influence pedestrian safety at midblock road segments. The knowledge of the risk factors specific to a location on the road network is helpful for incorporation and/or modification of the particular planning and design elements to reduce risk.

Analysis of Rear-End Conflicts in Urban Networks using Bayesian Networks

Crash analysis and modeling studies have provided insightful information on crash-contributing factors and the methodologies utilized provide evidence that they could also be beneficial for conflict analysis, as traffic conflict data share similar traits with crash data. In this study, a Bayesian network (BN) is estimated to comprehensively analyze rear-end conflict likelihood in an urban network, using disaggregate vehicle-by-vehicle data and the time-to-collision indicator to identify conflicts. The variables imported in the BN include (i) individual driver characteristics (e.g., speed); (ii) traffic operational characteristics (e.g., volume); and (iii) general characteristics (e.g., weather conditions). The inference analyses of the BN conducted to quantify the contributions of the variables affecting rear-end conflict likelihood in the urban network showed that rear-end conflict likelihood could be increased when the involved vehicles are of a different type, when the speed of the following vehicle is higher than the speed of the leading vehicle, when the individual speed is high when the individual headway is small, with a higher coefficient of variation of speed values, when the type of intersection nearest to the measuring point was a priority intersection, when the carriageway was of dual design, and when it was rainy. It was also shown that rear-end conflict likelihood increases during congestion and free-flow traffic. The findings of this study could be further developed to provide a good understanding of contributing factors to possible crashes in the urban network.

Understanding the Highway Safety Benefits of Different Approaches of Connected Vehicles in Reduced Visibility Conditions

This study evaluated the effectiveness of connected vehicle (CV) technologies in adverse visibility conditions using microscopic traffic simulation. Traffic flow characteristics deteriorate significantly in reduced visibility conditions resulting in high crash risks. This study applied CV technologies on a segment of Interstate I-4 in Florida to improve traffic safety under fog conditions. Two types of CV approaches (i.e., connected vehicles without platooning (CVWPL) and connected vehicles with platooning (CVPL) were applied to reduce the crash risk in terms of three surrogate measures of safety: the standard deviation of speed, the standard deviation of headway, and rear-end crash risk index (RCRI). This study implemented vehicle-to-vehicle (V2V) communication technologies of CVs to acquire real-time traffic data using the microsimulation software VISSIM. A car-following model for both CV approaches was used with an assumption that the CVs would follow this car-following behavior in fog conditions. The model performances were evaluated under different CV market penetration rates (MPRs). The results showed that both CV approaches improved safety significantly in fog conditions as MPRs increase. To be more specific, the minimum MPR should be 30% to provide significant safety benefits in terms of surrogate measures of safety for both CV approaches over the base scenario (non-CV scenario). In terms of surrogate safety measures, CVPL significantly outperformed CVWPL when MPRs were equal to or higher than 50%. The results also indicated a significant improvement in the traffic operation characteristics in terms of average speed.

Effects of variable speed limits on traffic operation characteristics and environmental impacts under car-following scenarios: Simulations in the framework of Kerner’s three-phase traffic theory

Variable speed limits (VSL) strategies have been demonstrated to successfully improve traffic dynamics characteristics. However, the effects of the strategies on overall traffic performance from the perspective of Kerner’s three-phase theory has yet to be needed. This paper attempts to present a comprehensive simulation study on this topic. The study investigates the effects of VSL strategies on traffic operation characteristics and environmental impacts by combining the Kerner–Klenov car-following model and a practical VSL control algorithm. Simulations have been carried out for different control scenarios. The simulation results quantitatively indicate how and to what extent the VSL strategies affect overall traffic performance, including fundamental diagram, local speed variance, VSP distributions and average exhaust emission rates. It is revealed that the implemented VSL strategies cannot either improve maximum flow rate or prevent from the occurrence of the F→S transition. However, VSL lead to a significant extension of occupancy range for synchronized flow phase and suppress speed variability in traffic stream in congested traffic state, especially in the jam phase and the transitional state S→J. Moreover, the VSL strategies is able to reduce traffic emissions by improving VSP distributions and average emission rates in the synchronized and jam phases. These findings suggest that the perspective of Kerner’s three-phase theory can be added to the development of VSL control algorithms which aim to achieve high traffic efficiency with minimum traffic environmental impacts.

Cargo cycles for local delivery in New York City: Performance and impacts

This study aimed to evaluate the operating performance of human-powered cargo cycles compared to motorized delivery vehicles for local delivery in Manhattan, New York City (NYC) in the United States of America (USA). While many recent studies have more broadly examined cost and impact tradeoffs in the use of cargo cycles for last-mile parcel delivery, the primary focus of this study was to understand how human-powered cargo cycle moving and parking behaviors differ from those of motorized vehicles in NYC. The study employed spatial and statistical analysis methods to directly estimate traffic performance measures using GPS data collected from two local operators. Traffic operating characteristics - including corridor travel speeds, trip delays, delivery times, and travel distances were examined to compare goods movements and parking behaviors for each vehicle type. Street and parking space, CO2, and particulate matter emissions savings were also estimated for alternate urban delivery vehicle replacement scenarios for one of the participating operators. Overall, results from this study suggest that cargo cycles can provide environmental benefits and offer a competitive last-mile delivery option for some local operators in very congested cities like NYC.

Research on Information Demand and Publishing Framework about Public Infrequent Events Based on Residents Feature

Traffic operation characteristics of the city gradually changed from simplicity to comprehensiveness. In this paper, different influence of episodic events was analyzing from angle of the students, commuter, retirees, according to the common accidental event types of public traffic. Combined with the SP public small sample survey, different residents requirement on information about type, location, prompting time was analyze and contingency information request form was constructed. Besides, the best information publishing channels of different groups was discussed and contingency framework was built. In addition, mobile phone APP mobile terminal was developed and the framework of information release was constructed. All in all, the goal is to solve the conflict between city residents travel demand and travel information accessibility, timeliness, accuracy, and to improve transportation efficiency.

Modelling Motorway Accidents using Negative Binomial Regression

This paper investigates motorway safety by developing accident prediction models that link accident frequencies to their non-behavioral contributing factors, including traffic conditions, geometric and operational characteristics of road, and weather conditions. The study used a sample of accidents occurred from 2004 through 2010 on a 74 km long section of Auckland motorway. A number of accident prediction models were developed and assessed for their predictive ability using negative binomial regression models under three categories: first for the whole of the motorway, second for rural and urban motorway segments separately and third for motorway segments without ramp, with on-ramp and with off-ramp separately. The results uncovered the safety impacts of different non- contributing factors, in which segment length, AADT per lane and the number of lanes always have the most profound effects on accident frequency. The validation tools were applied to examine the ability of models to predict accidents.

Research on Traffic Safety of Freeway Upgrade Section

In this paper, upgrade section was classified based on linear combination form and whether or not constructing the climbing lane. Traffic accident characteristics on the upgrade section were analyzed from the viewpoint of vehicle type, accident type and time distribution. For in-depth study of accident causes, the paper analyzed traffic flow operation characteristics and discussed main influencing factors of traffic flow situation. In addition, the influence zone of upgrade section was put forward and defined. This laid good foundation for follow-up study on influence mechanism between microscopic traffic flow operation characteristics and traffic safety. Finally, the corresponding safety improvement measures were proposed.