Highway Segments Research Articles

Experimental Investigation into Driver Behavior along Curved and Parallel Diverging Terminals of Exit Interchange Ramps

Current design manuals provide guidance on how to design exit ramps to facilitate driving operations and minimize the incidence of crashes. They also suggest that interchanges should be built along straight roadway sections. These criteria may prove ineffective in situations where there is no alternative to terminals being located along curved motorway segments. The paper investigates driving behavior along parallel deceleration curved terminals, with attention paid to the difference in impact between terminals having a curvature which is the same sign as the motorway segment (i.e., continue design), and those having an opposite curvature (i.e., reverse design). A driving simulation study was set up to collect longitudinal and transversal driver behavioral data in response to experimental factor variations. Forty-eight drivers were stratified on the basis of age and gender, and asked to drive along three randomly assigned circuits with off-ramps obtained by combining experimental factors such as motorway mainline curve radius (2 values), terminal length (3), curve direction (2), and traffic conditions (2). The motorway radius was found to be significant for drivers’ preferred speed when approaching the terminal. Terminal length and traffic volume do not have a significant impact on either longitudinal or transversal driver outputs. However, the effect of curve direction was found to be significant, notably for reverse terminals which do not compel drivers to select appropriate speeds and lane change positions. This terminal type can give rise to critical driving situations that should be considered at the design stage to facilitate the adoption of appropriate safety countermeasures.

Auction based Energy-Efficient Cooperative Relay Scheduling in Bidirectional Highway Scenarios for VANET

In a Vehicular Adhoc Network (VANET), Road Side Units (RSUs) deployed along the highways are generally dispossessed from a permanent connection to grid-power and mostly the RSU operations are sustainable on alternative sources such as solar power, wind power, etc. The power consumption of RSU is dominated by the downlink (RSU-to-vehicle) communication cost and it is a function of RSU to vehicle distance. Moreover, the RSUs cannot cover entire highway segment due to high deployment cost and they leave an outage area in between neighboring RSUs. In this case, a target vehicle entering into an outage area with unserved requests can be served by selecting relay (store-carry-forward) vehicles. This paper proposes RSU assisted relay scheduling algorithm for faster data retrieval to the target vehicle while minimizing the RSU energy consumption. Firstly, a time slot based relay scheduling problem is formulated and then the NP-hardness of the problem is proved. Secondly, a greedy algorithm (GA) and a forward relay scheduler (FRS) are proposed to schedule the relay vehicles in forward direction (i.e. target moving direction). The GA selects the relay vehicles which are near to RSU and takes less time to reach the target vehicle. The FRS apply the concepts of Auction Theory to optimally assign the relay vehicles to time slots, that can jointly minimize RSU energy consumption and end-to-end delay to the target vehicle. Here, the Auction process allows the relay vehicles to bid for time slots by using their local information such as speed, location, cooperative cache size, direction, etc. Furthermore, the proposed Auction based RSU assisted relay scheduling (RRS) triggers the neighboring RSUs which can cooperate by sharing unserved data and schedule the relay vehicles driving in both forward and backward (i.e. opposite to target) directions in a bidirectional highway. Simulation results show that the efficacy of the proposed algorithms over other relay scheduling algorithms with respect to energy consumption, end-to-end delay and residual data delivery.

Analysis on the Control Effect of Enhanced Cushion on the Difference Settlement of New and Old Subgrade in Soft Foundation Area

In order to reduce the differential settlement of the extended urban highway splicing segment, we monitored the junction of the new and old subgrade in the practical engineering, analyzed the value of pavement deformation. It found that the smaller the modulus ratio of the new and old subgrade, the greater the non-uniform settlement value. At the same time, the uneven settlement and lateral displacement of the road can be effectively reduced by using the pile-reinforced cushion structure. So the results show that the pile-reinforced cushion structure can effectively reduce the uneven settlement and lateral displacement of the old and new roads, strengthen the links between old and new roads; To enhance the stability of subgrade, it can be used for widening the old road in soft soil foundation.

Developing the guidelines for managing autonomous vehicle flows on congested highways: A case study of MD-100

Recognizing the need for responsible highway agencies to effectively manage emerging autonomous vehicles (AV) flows in contending with daily recurrent congestion, this study presents a systematic procedure for understanding the impacts of AV flows on traffic conditions under different AV behavioral mechanisms (i.e., car-following and lane-changing), and different penetration rates. Research results show that the presence of AV flows, depending on their adopted behavioral mechanisms, may have significant (either positive or negative) impacts on the overall traffic conditions. Hence, it is essential for responsible highway agencies to have proper operational guidelines to manage and coordinate AV flows. To demonstrate the proposed methodology, this study has carried out extensive simulation experiments using a congested segment of the MD-100 network (a multilane highway segment located in Maryland) under various AV penetration rates and observable behavioral patterns. The collected Measures of Effectiveness highlight that at each AV penetration level there exists a set of optimal behavioral patterns for the AV flows to coordinate with non-AV flows via the Vehicle to Infrastructure or Vehicle to Vehicle infrastructure so as to maximize the roadway capacity and minimize the resulting highway congestion.

Introducing a New Apparatus for Designating Two-Lane Highway Passing and No-Passing Zones

Two-lane highways constitute a large proportion of the U.S. highways. An essential component needed in the design of safe two-lane highways is the passing sight distance (PSD). Otherwise, insufficient PSDs lead to passing-related crashes and, therefore, no-passing zones ought to be marked. This research involves the development of a new apparatus of the two-vehicle method, which is used for measuring the PSD in the field. That is to replace the defunct apparatus used by the Wyoming Department of Transportation (WYDOT). To the best of the authors’ knowledge, the introduced apparatus is the most up-to-date system and addresses shortcomings of previous research. The two-vehicle method involves two successive vehicles spaced at a gap, equivalent to PSD, and both vehicles travel at the speed limit. The driver of the rear vehicle operates a switch when the lead vehicle becomes invisible because of sight obstructions, such as vegetation, signaling the beginning point of the no-passing zone. Similarly, the switch is operated when the lead vehicle returns to view to designate the endpoint of the no-passing zone. The apparatus is composed of vehicle-to-vehicle radio communication devices, global positioning system devices, the switch and computers with graphical user interfaces to record and display the data. Testing was conducted on two two-lane highway segments. As per the results, overall discrepancies between WYDOT’s no-passing zone markings and those designated by the apparatus, developed, ranged from 1% to 7%. This research lays the foundation for a future study involving the development of a cutting-edge prototype.

Towards a more inclusive and safe design of horizontal curves: Exploring the association between curve features, reliability measures, and safety

Reliability analysis has been advocated as a robust methodology to quantify the risk (known as the probability of non-compliance, Pnc) associated with design limitations such as insufficient sight distance on horizontal curves. This risk represents the probability that the current design (e.g., available sight distance) would fail to meet the requirements of the driving population (e.g., required sight distance). Although previous work has quantified the risk and established links between Pnc and safety, Pnc remains a statistical measure that is not informative enough to roadway designers. To overcome this limitation, the impacts of geometric design attributes on the Pnc as well as the direct and indirect (through the impacts on Pnc) impacts of those attributes on safety need to be modelled and understood. To achieve the aforementioned objective, this paper proposes the adoption of Structural Equation Modelling (SEM) to simultaneously model the relationships mentioned above using data collected on horizontal curves in Alberta, Canada. Light Detection And Ranging (LiDAR) data was first used to extract curve information and assess the 3D available sight distance on 244 curved highway segments. Reliability analysis was then used to quantify the Pnc associated with each curved segment. SEM was then used to test the impacts of curve geometric and traffic attributes on Pnc as well as the direct and indirect impacts those variables have on collision frequency. The results showed that Pnc, curve length, and traffic volume all had statistically significant effects on collision frequency. Curve attributes such as the curve’s deflection angle and chord length had statistically significant impacts on Pnc. Both curve deflection angle and the chord length were also found to have an indirect influence on collisions, which was mediated through Pnc. The findings emphasize the importance of considering other curve attributes when designing horizontal curves instead of focusing entirely on the curve radius. The findings of this research provide insights into the indirect impacts of curve attributes of horizontal curves on safety. This could help designers consider curve features that have the highest impacts on non-compliance and safety levels.

Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy

Highway system is experiencing increasing traffic congestion with fast-growing number of vehicles in metropolitan areas. Implementing traffic management strategies such as utilizing the hard shoulder as an extra lane could increase highway capacity without extra construction work. This paper presents a method of determining an optimal traffic condition and speed limit of opening hard shoulder. Firstly, the traffic states are clustered using K-Means, mean shift, agglomerative and spectral clustering methods, and the optimal clustering algorithm is selected using indexes including the silhouette score, Davies-Bouldin Index and Caliski-Harabaz Score. The results suggested that the clustering effect of using K-Means method with three categories is optimal. Then, cellular automata model is used to simulate traffic conditions before and after the hard shoulder running strategy is applied. The parameters of the model, including the probabilities of random deceleration, slow start and lane change, are calibrated using real traffic data. Four indicators including the traffic volume, the average speed, the variance of speed, and the travel time of emergency rescue vehicles during traffic accident obtained using the cellular automata model are used to evaluate various hard shoulder running strategies. By using factor analysis and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods, the optimal traffic condition and speed limit of opening hard shoulder could be determined. This method could be applied to highway segments of various number of lanes and different speed limits to optimize the hard shoulder running strategy for highway management.

Boundary Observer Design for Stochastic Phase Transition Models of Nonequilibrium Traffic Flow

State estimate of traffic flow of a freeway segment is considered by using the method of boundary observer design. The observer control and measurements are all located at the boundaries. The nonequilibrium traffic flow dynamics are modeled as a Markov jump linear hyperbolic system with phase transitions. Based on the Lyapunov techniques, the sufficient conditions of matrix inequalities are derived for the exponentially mean-square static as well as the dynamic boundary observer design. Using real traffic data of vehicle trajectories from the Next Generation Simulation (NGSIM) project, the developed stochastic phase transition model of the nonequilibrium traffic flow is calibrated. Moreover, the simulation results illustrate the effectiveness of the boundary observer design for traffic estimation.

An Artificial Intelligence-Based Approach for the Controlled Access Ramp Metering Problem

The ever-increasing demand for transportation of people and goods as well as the massive accumulation of population in urban centers have increased the need for appropriate infrastructure and system development in order to efficiently manage the constantly increasing and diverse traffic flows. Moreover, given the rapid growth and the evolution of Information and Communication Technologies (ICT), the development of intelligent traffic management systems that go beyond traditional approaches is now more feasible than ever. Nowadays, highways often have sensors installed across their range that collect data such as speed, density, direction and so on. In addition, the rapid evolution of vehicles with installed computer systems and sensors on board, provides a very large amount of data, ranging from very simple features such as speed, acceleration, etc. to very complex data like the driver’s situation and driving behavior. However, these data alone and without any further processing, cannot solve the congestion problem. Therefore, the development of complex computational methods and algorithms underpins the chance to process these data in a fast and reliable way. The purpose of this paper is to present a traffic control ramp metering (RM) method based on machine learning and to study its impact on a selected highway segment.

Effectiveness of Active Luminous Lane Markings on Highway at Night: A Driving Simulation Study

Road lane markings play an essential role in maintaining traffic order and improving traffic safety and efficiency. Active luminous lane markings have emerged with advances in technology recently. However, it is still not completely clear what impact their application will have on drivers. This paper aimed to study the effectiveness of active luminous lane markings on highways at night. A driving simulation experiment was carried out based on advanced driving simulators at Tongji University. The driving simulation experiment involved 31 participants and 9 simulation scenes with 6 different types of lane markings models and the same 2-way highway segment, which was 5300-m long with four 3.75-m wide driving lanes. The study participants drove through the simulated highway while the vehicle operation data and the driver’s eyes changing data were continuously captured. Overall, the pupil area change rate, steering wheel speed, brake pedal force, gas pedal, lane departure, and operating speed indicators were selected to evaluate the effectiveness of the active luminous lane markings. The results are shown as follows: (1) the active luminous lane markings have excellent visual recognition performance at night. Compared with the passive luminous lane markings, the active luminous markings can reduce the mental and physical loads of drivers, increase the early braking distance significantly, improve the lane-keeping ability and smooth the operating speed; (2) for the specific parameter settings of the active luminous lane markings at night, the yellow lane markings are better than the white ones, the point-line-type lane markings are superior to the conventional-type ones, and the blinking frequency is reasonable to set, at a moderate level, as 40 times per min. The results suggest that there are positive effects of active luminous lane markings on the promotion of highway traffic safety and efficiency at night, providing theoretical support for the popularization and application of active luminous road lane markings.

A multi-vehicle communication system to assess the safety and mobility of connected and automated vehicles

Connected and automated vehicles (CAVs) are expected to improve both traffic safety and efficiency by reducing the human driver errors. Recently, many researchers have focused on the simulation-based studies in order to evaluate the benefits of CAVs due to the lack of real-world data. However, none of the previous studies have attempted to differentiate the benefits of CAVs over automated vehicles (AVs) by incorporating multiple preceding vehicle information (i.e., acceleration, position, etc.). This paper aims to fill the existing gap by utilizing separate car-following models for both CAVs and AVs in order to approximate their driving behavior in the Aimsun Next simulation platform. Additionally, a different car-following model is used for the connected vehicles (CVs) without automation by addressing the human driver compliance factor. This study also utilizes mixed penetration of CAV and CV with no automation. A well calibrated and validated simulation testbed is developed for SR417 in Orlando, Florida which is the base scenario in this study. To this end, the impact of CAVs, AVs, and CVs are evaluated based on both traffic efficiency (i.e., travel time) and safety (i.e., traffic conflicts) under various market penetration rates (MPRs). The traffic efficiency results show that travel time is significantly reduced for any MPRs of CAVs, AVs, and the mixture of CVs and CAVs compared to the base scenario. A generalized estimating equation (GEE) model is developed to quantify the travel time improvement for CAVs, AVs, and the mixture of CVs and CAVs. The results suggest at least 20% penetration is required for CAVs to get travel time improvement while 40% penetration is needed for AVs. Also, CAV significantly outperforms AV for the same MPRs. For the safety assessment, traffic conflicts are estimated by using different surrogate measures i.e., time-to-collision (TTC) and time exposed time-to-collision (TET). The results imply that crash risk is significantly reduced for CAVs, AVs, and the mixture of CVs and CAVs scenarios compared to the base condition. A Bayesian zero-inflated negative binomial model is developed in order to model the number of traffic conflicts as a function of MPRs of CAVs, AVs, mixture of CVs and CAVs, and traffic parameters. The results confirm that CAVs are more efficient in reducing crash risk compared to AVs for the same MPR. The mix penetration rate of CV (60%) and CAV (20%) shows almost similar reduction of crash risk with the 80% MPR of AV. Also, crash risk analysis based on different vehicle types shows that CAVs driving behavior is safer compared to the AVs. Finally, the results of this study indicate a significant improvement of both traffic efficiency and safety by implementing CAV with multivehicle communication system on the freeway segments.

ОЦЕНКА СОСТОЯНИЯ ЗАГРЯЗНЕНИЯ АТМОСФЕРНОГО ВОЗДУХА НА СЕГМЕНТЕ ГОРОДСКОЙ МАГИСТРАЛИ (НА ПРИМЕРЕ ПРОСПЕКТА МИРА В ГОРОДЕ ОМСКЕ)

The article presents the results of experimental and theoretical studies aimed at performing a qualitative assessment of the impact of traffic management on the level of air pollution from traffic flows in different sections of one segment of the city highway. Assessment of the state of atmospher-ic air pollution on a highway with heavy traffic (Pr. Mira, Omsk city) was carried out on the basis of determination of carbon monoxide (CO) in the air as one of the main components of the exhaust gases of the traffic stream. Calculations are graphically presented, indicating the unevenness of the amount of pollutant emissions by one passenger car moving along a conditional segment of the city highway with various variations in traffic conditions due to traffic management.

Soil properties limiting vegetation establishment along roadsides.

Roadside vegetation provides a multitude of ecosystem services, including pollutant remediation, runoff reduction, wildlife habitat, and aesthetic scenery. Establishment of permanent vegetation along paved roads after construction can be challenging, particularly within 1 m of the pavement. Adverse soil conditions could be one of the leading factors limiting roadside vegetation growth. In this study, we assessed soil physical and chemical properties along a transect perpendicular to the road at six microtopographic positions (road edge, shoulder, side slope, ditch, backslope, and field edge) along two highway segments near Beaver Crossing and Sargent, NE. At the Beaver Crossing site, Na concentration was 81 times, exchangeable Na 66 times, and cone index (compaction parameter) six times higher at the road-edge position (closest to the paved road and with sparse vegetation) compared to positions with abundant vegetation (ditch or field edge). At the Sargent site, Na concentration was 111 times, exchangeable Na 213 times, and cone index up to two times higher at the road-edge position compared with ditch or field-edge positions. Likewise, electrical conductivity was higher and macroaggregation and water infiltration were lower at the road edge than at the ditch or field-edge positions. Soil properties improved with increasing distance from the road. Exchangeable Na percentage and cone index at the road-edge position exceeded threshold levels for the growth of sensitive plants. Thus, high Na concentration and increased compaction at the road edge appear to be the leading soil properties limiting vegetation establishment along Nebraska highways.

Investigation of the Impact of the Guangzhou-Lechang Expressway Segment at the Pajiang River Detention Basin to Its Normal Flood Prevention Operation

Based on the existing relevant information, the paper investigates the occupation situation of the segment of the Guangzhou-Lechang expressway in the Pajiang River detention basin and analyzes the relationship of the water conservancy facility construction to the planning of the detention basin. One-dimensional and two-dimensional mathematical models are established to evaluate the construction effect on flood control in the detention basin under the normal use condition of the Pajiang River. The results show that the construction occupies 0.09% regulation and storage volume of the detention basin, which leads to the following impacts to its flood prevention operation: the starting time of the detention basin delayed 3~12 minutes, the average discharge of the Jiangkouxun section in the main river of the Beijiang River increased 12m3/s, and the average discharges of the Tiechuang-ao section of the Pajiang River and the Changbu section of the Yuantan River in the detention basin decreased 14 m³/s and 13 m³/s, respectively. Meanwhile, the highest water level of the Jiangkouxu Station and the peak discharge of the Shijiao Station remain almost the same. Therefore, as the conclusion, the engineering construction of the expressway segment only produces a minor impact on flood control of the Beijiang River. This investigation provides the technical basis for evaluation of the impact to flood prevention by highway construction for similar projects and will be useful for decision-making of the relevant planning agencies.

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient ( ${\text C}_{d}$ ) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead.

Developing a macroscopic model based on fuzzy cognitive map for road traffic flow simulation

Fuzzy cognitive maps (FCM) have been broadly employed to analyze complex and decidedly uncertain systems in modeling, forecasting, decision making, etc. Road traffic flow is also notoriously known as a highly uncertain nonlinear and complex system. Even though applications of FCM in risk analysis have been presented in various engineering fields, this research aims at modeling road traffic flow based on macroscopic characteristics through FCM. Therefore, a simulation of variables involved with road traffic flow carried out through FCM reasoning on historical data collected from the e-toll dataset of Hungarian networks of freeways. The proposed FCM model is developed based on 58 selected freeway segments as the “concepts” of the FCM; moreover, a new inference rule for employing in FCM reasoning process along with its algorithms have been presented. The results illustrate FCM representation and computation of the real segments with their main road traffic-related characteristics that have reached an equilibrium point. Furthermore, a simulation of the road traffic flow by performing the analysis of customized scenarios is presented, through which macroscopic modeling objectives such as predicting future road traffic flow state, route guidance in various scenarios, freeway geometric characteristics indication, and effectual mobility can be evaluated.

A Control-Theoretic Approach for Scalable and Robust Traffic Density Estimation Using Convex Optimization

Monitoring and control of traffic networks represent alternative, inexpensive strategies to minimize traffic congestion. As the number of traffic sensors is naturally constrained by budgetary requirements, real-time estimation of traffic flow in road segments that are not equipped with sensors is of significant importance - thereby providing situational awareness and guiding real-time feedback control strategies. To that end, firstly we build a generalized traffic flow model for stretched highways with arbitrary number of ramp flows based on the Lighthill Whitham Richards (LWR) flow model. Secondly, we characterize the function set corresponding to the nonlinearities present in the LWR model, and use this characterization to design real-time and robust state estimators (SE) for stretched highway segments. Specifically, we show that the nonlinearities from the derived models are locally Lipschitz continuous by providing the analytical Lipschitz constants. Thirdly, the analytical derivation is then incorporated through a robust SE method given a limited number of traffic sensors, under the impact of process and measurement disturbances and unknown inputs. The estimator is based on deriving a convex semidefinite optimization problem. Finally, numerical tests are given showcasing the applicability, scalability, and robustness of the proposed estimator for large systems under high magnitude disturbances, parametric uncertainty, and unknown inputs.

Output feedback control of two-lane traffic congestion

This paper develops output feedback boundary control to mitigate traffic congestion of an unidirectional two-lane freeway segment. The macroscopic traffic dynamics are described by the Aw–Rascle–Zhang (ARZ) model respectively for both the fast and slow lanes. The traffic density and velocity of each of the two lanes are governed by coupled 2 × 2 nonlinear hyperbolic partial differential equations (PDEs). Lane-changing interactions between the two lanes lead to exchanging source terms between the two pairs second-order PDEs. Therefore, we are dealing with 4 × 4 nonlinear coupled hyperbolic PDEs. Based on driver’s preference for the slow and fast lanes, a reference system of lane-specific uniform steady states in congested traffic is chosen. To stabilize traffic densities and velocities of both lanes to the steady states, two distinct variable speed limits (VSLs) are applied at outlet boundary, controlling the traffic velocity of each lane. Using backstepping transformation, we map the coupled heterodirectional hyperbolic PDE system into a cascade target system, in which the traffic oscillations are damped out through actuation of the velocities at the downstream boundary. Two full-state feedback boundary control laws are developed. We also design a collocated boundary observer for state estimation with sensing of the densities at the outlet. Output feedback boundary controllers are obtained by combining the collocated observer and full-state feedback controllers. The finite time convergence to equilibrium is achieved and a performance improvement in fuel consumption, drivers’ comfort and total travel time is demonstrated with the proposed output feedback controllers for some parameter choice, compared with the open-loop system. The proposed output feedback design is also validated for different congested traffic scenarios and compared with one-lane backstepping and PI control designs.

Who overtakes more? Explanatory analysis of the characteristics of drivers from low/middle and high-income countries on passing frequency

The passing manoeuvre requires a driver to make decisions and take actions which are dependent on his/her behavioural characteristics and driving ability. However, previous works on passing rate models have exclusively considered geometric and traffic-related variables. This study aims at bridging this gap by investigating the influence of driver profile (i.e., age, gender, nationality - Italian or Iranian - aggressive driving scores, driving exposure) on passing frequency. A driving simulation experiment involving 54 drivers (36 Italians, 18 Iranians) was conducted along a 6.67 km segment of a two-lane rural highway with passing manoeuvres permitted along 25% of its length. Controlled factors included traffic flow and speed in the oncoming direction, and speed in the driver direction, with a total of 27 scenarios assigned to drivers based on a 33 confounded factorial design. A Poisson regression model was used to investigate the significance of independent variables. Age and gender and their interaction term were significant, thus the effects of age and gender on the number of passing manoeuvres are mutually interdependent. Furthermore, drivers who drive less often completed fewer overtaking manoeuvres. Sensitivity analyses were carried out to understand the magnitude of change in passing frequency attributable to a variation in the explanatory variables. The findings suggest that driver characteristics have a significant effect on passing frequency and should be considered when conducting a performance and safety evaluation of two-lane roads.

A crash risk identification method for freeway segments with horizontal curvature based on real-time vehicle kinetic response

With the development and maturation of vehicle-based data acquisition technology, in-vehicle data is increasingly being used to explore road safety. This paper reports on research that analyzed the real-time tire force data (kinetic response) obtained from vehicle kinetic experiments, and constructed a new approach for identifying the high-risk of crashes on freeway segments with horizontal curvature. First, the road was divided into 1km units. Then, taking into account the characteristics of freeway alignment, each segment with horizontal curve was selected as the object of subsequent analysis. Automotive instrumentation was used to obtain a measure of tire force in the course of normal driving. The entire data set was preprocessed according to rate of change and the density of the data was reduced. By defining the outliers of the kinetic data and conducting factor analysis, two representative crash risk indicators of longitudinal and lateral stability were obtained. Negative binomial regression model (NBR model) and random effects negative binomial regression model (RENBR model) were constructed and jointly applied based on the new indicators to predict the risk value of horizontal curve segments. The method showed good prediction performance (71.8 %) for high-risk road segments with design flaws, but the predicted effect for low-risk road segments was not ideal. This study not only illustrated the effectiveness of in-vehicle data in assessing road crash risk by coupling multiple kinetic parameters, but also provided support for freeway safety research using surrogate measures of risk when there is a lack of crash statistics.