Highway Speeds Research Articles

Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach

This paper designs a systemic framework to quantify speed reduction induced by traffic incidents using a causal inference framework. The results can provide a reference to traffic managers for evaluating incident severities, thus take proper control measures after the incident in order not to underestimate or overestimate the negative impact. A two-phase scheme is proposed, including impacted region determination and speed reduction quantification. We first propose a Frame Region (FR) method, based on the shockwave propagation, to determine the spatiotemporal impacted region (SIR) using speed map. It is worth-noting that we design a statistical experiment to prove the rationality of congestion threshold selection. Secondly, we introduce a causal inference method for identifying the matched freeway segments. The traffic condition of finally matched freeway segments can be served as non-incident traffic condition of the incident occurred location, which contributes to quantifying the incident impact on speed reduction. We further demonstrate the proposed method in a case study by taking advantage of an incident record and related real freeway speed data in China. An interesting observation is that, along with the freeway segments away from the incident location, the congestion duration time of different freeway segments firstly rises and then decreases. The case study also illustrates the impact of incident on speed lasts almost 3 h and the congestion caused by the incident spreads 11 km, while the average causal effect of incident on all the impacted freeway segments is 42.3 km/h.

Design Strategy of Highway Speed Transition Section

In this paper, combined with the relevant speed theory and characteristics of the law, the current highway speed transition design problems are studied and analyzed. In the process of specific analysis, mainly combined with the characteristics of different types of highway speed changes and road section design requirements, this paper studies and analyzes the design methods of different types of highway speed transition section. And on this basis, according to the design principles and requirements of highway operation speed transition section, the paper summarizes the matters needing attention in the design of highway operation speed transition section, in order to provide certain reference value for relevant personnel.

BIOMECHANICS OF PENETRATING TRAUMA AND INJURY MITIGATING PROTECTIVE MEASURES IN GUARDRAIL MOTOR VEHICLE CRASHES

The objective of the present study is two-fold. First, the elucidation of the biomechanics of penetrating trauma as a result of guardrail intruding into the occupant compartment. Second, the evaluation of the biomechanical efficacy of hybrid tension-compression guardrails to better protect occupants. The nine fatally guardrail penetrating crashes occurred between 2016 and 2019 were analyzed to study the mechanism of injuries. Four car-to-guardrail crash tests were conducted using a hybrid guardrail that integrated the commonly used W-beam with a new design of tension-based end terminal. The test included the impact of a bogey-type platform, small sedan vehicles, and a pick-up truck at highway speeds onto the guardrail. The impact orientation was varied to simulate the frontal and oblique corner crashes with a speed ranging from 90 to 111 kph. The real-world studies showed that the fatal injuries were due to impaling guardrail regardless of vehicular speed and size. The occupants not in the trajectory of the guardrail in the same vehicle sustained minor injuries despite experiencing a similar energy level. In these cases, the crash severity was survivable without the guardrail penetration. The mean pre-impact speed, change in speed, and vehicular acceleration was 117 kph, 20 kph, and 97 m/sec2, respectively. The hybrid guardrail system deflected the vehicle without any penetration into the occupant compartment. The mean peak accelerations in crash tests were below injurious threshold levels. The present research shows that the hybrid guardrail system not only eliminated the intrusion into occupant survival space but also deflected the vehicle.

The feasibility of heavy battery electric trucks

Summary Research on the decarbonization of transport often concludes that heavy battery electric trucks are infeasible due to the incompatibility of long driving distance with high energy use and low specific energy and high costs of batteries. However, emphasis is often placed on battery electric range matching that of diesel trucks, instead of overall competitiveness. We model battery electric trucks that use high-power fast charging, enabling smaller batteries and showing that the economics of battery electric trucks per ton-kilometer improves with greater weight, driven by increasing load capacity as well as increased energy savings as a function of weight. Furthermore, we show that previous findings that the competitiveness per kilometer is worse for heavy trucks than for lighter trucks are very sensitive to assumptions about the battery cost per kWh and lifetime of the battery pack. Given the rapid development of batteries, we conclude that the economic feasibility of heavy battery electric trucks might have been generally underestimated.

Highway Speed Prediction Using Gated Recurrent Unit Neural Networks

Movement analytics and mobility insights play a crucial role in urban planning and transportation management. The plethora of mobility data sources, such as GPS trajectories, poses new challenges and opportunities for understanding and predicting movement patterns. In this study, we predict highway speed using a gated recurrent unit (GRU) neural network. Based on statistical models, previous approaches suffer from the inherited features of traffic data, such as nonlinear problems. The proposed method predicts highway speed based on the GRU method after training on digital tachograph data (DTG). The DTG data were recorded in one month, giving approximately 300 million records. These data included the velocity and locations of vehicles on the highway. Experimental results demonstrate that the GRU-based deep learning approach outperformed the state-of-the-art alternatives, the autoregressive integrated moving average model, and the long short-term neural network (LSTM) model, in terms of prediction accuracy. Further, the computational cost of the GRU model was lower than that of the LSTM. The proposed method can be applied to traffic prediction and intelligent transportation systems.

Pavement Distress and Debris Detection using a Mobile Mapping System with 2D Profiler LiDAR

Regular pavement monitoring over highways and airport runways is vital for public agencies to ensure the safe riding of vehicles and aircrafts. Highways are mostly subject to cracking and potholes along with a few instances of debris around construction work zones. Airports are also concerned with debris but have much lower tolerance for the presence of foreign object debris (FOD) that could possibly damage the aircraft. LiDAR is rapidly emerging in a variety of mobile mapping systems (MMS) and will likely be integrated into many transportation vehicles over the next decade for pavement inspection. This paper proposes a unique algorithm for pavement surface inspection with the help of MMS driven at highway speeds. The study analyzed LiDAR data acquired for 8 mi of highway collected at approximately 55 to 60 mph. This study indicates that an adequately designed MMS along with the proposed algorithm can efficiently detect pavement anomalies as small as 2 cm in the form of cracking, potholes, surface debris, or any combination of these. This is more than sufficient for highways, where debris such as ladders and tires are an order of magnitude larger. For evaluating the effectiveness of detecting smaller airport FOD, a validation dataset was created by driving the MMS at 15 mph adjacent to a debris field of 50 sample pieces of FOD collected from an airport. The study found that 100% of the FOD items larger than 2 cm in size (12 out of 50 samples) were detected successfully at 15 mph. Both datasets suggest that MMS LiDAR is sufficient for pavement inspection and as sensor fidelity increases, even small FOD will be able to be detected with the algorithm proposed in this paper.

Examining vehicular speed characteristics through divergences from prior distributions

A variety of approaches, within literature, has been conducted to interpret vehicular speed characteristics. This study turns the attention to the entropy-based approaches, and thus focuses on the maximum entropy method of statistical mechanics and the Kullback–Leibler (KL) divergence approach to examining the vehicular speeds. The vehicle speeds at the selected highway are analyzed in order to find out the disparities among them. However, it is turned out that the speed dynamics could not be distinguished over the speed distributions; hence the maximization of Shannon entropy seems insufficient to compare the speed distributions of each data set. For this reason, the KL divergence approach was performed. This approach displays the comparison, among the speed distributions, based on two prior distribution models, i.e., uniform and Gauss. The examination of the trends of KL divergences obtained from both distributions was made. It was concluded that the KL divergence values for the highway speed data sets ranged between about 0.53 and 0.70 for the uniform case, while for the Gaussian case the obtained values are between 0.16 and 0.33. The KL divergence trends for the real speeds were obtained analogous for both cases, but they differed significantly when the synthetic data sets were employed. As a result, the KL divergence approach proves suitable as an appropriate indicator to compare the speed distributions.

Joint Impact of Rain and Incidents on Traffic Stream Speeds

Unpredictable and heterogeneous weather conditions and road incidents are common factors that impact highway traffic speeds. A better understanding of the interplay of different factors that affect roadway traffic speeds is essential for policymakers to mitigate congestion and improve road safety. This study investigates the effect of precipitation and incidents on the speed of traffic in the eastbound direction of I-64 in Virginia. To the best of our knowledge, this is the first study that studies the relationship between precipitation and incidents as factors that would have a combined effect on traffic stream speeds. Furthermore, using a mixture model of two linear regressions, we were able to model the two different regimes that the traffic speed could be classified into, namely, free-flow and congested. Using INRIX traffic data from 2013 through 2016 along a 25.6-mi section of Interstate 64 in Virginia, results show that the reduction of traffic speed only due to incidents ranges from 41% to 75% if the road is already congested. In this case, precipitation was found to be statistically insignificant. However, regardless of the incident impact, the effect of light rain in free-flow conditions ranges from insignificant to a 4% speed reduction while the effect of heavy rain ranges from a 0.6% to a 6.5% speed reduction when the incident severity is low but has a roughly double effect when the incident severity is high.

Control Architecture for Cooperative Autonomous Vehicles Driving in Platoons at Highway Speeds

The situation in which a vehicle has to avoid a collision with an obstacle can be difficult to realise in optimum conditions when the roads are crowded. This paper uses the advantages of vehicle grouping and vehicle-to-vehicle (V2V) communication, and proposes a control architecture, which ensures a safe merging between the vehicles from two platoons. The architecture is formed by three layers, with the following tasks: i) to analyse the environment and to decide the best action for a certain vehicle, ii) to plan the new trajectory, and iii) to follow it at an imposed velocity or distance to the vehicle in front. The vehicles are equipped with a trajectory planner designed using two methods: the first one is based on a polynomial equation, and the second one is based on the model predictive control (MPC) algorithm. Each vehicle is also equipped with a trajectory follower, which has a cooperative adaptive cruise control (CACC) functionality based on a distributed model predictive control (DMPC) formulation. Also, the paper proposes a solution to compensate the data-packet-dropouts that are induced by the wireless communication network used to exchange information between vehicles. Moreover, to accommodate various realistic scenarios in the same control framework, the cost function for the DMPC algorithm was designed to take into account different communication topologies. The proposed architecture was tested in a simulation scenario, in which two platoons have to merge in order to avoid a fixed obstacle and the results show its efficiency.

Survey on Big Data Techniques in Intelligent Transportation System (ITS)

Big Data is an evolving exemplar moreover, particularly within in the transportation system, has now become a powerful enticement of global interest. On the basis, Big Data is often seen intact undertaking whereas transit system fore efficiently handle utterly the information needed by aforementioned part to provide secure, unstained including reliable modes of transit, and to create custom the recipient's transport experience. By having access to centralized services, a smart city enhances the effectiveness of its inhabitants. Intelligent Transportation Systems (ITS) play an essential role in the transformation of a cosmopolitan environment into a digital city. With the last two decennary, numerous ITS technologies have been deployed, such as city-wide traffic regulation control, smart parking, general transport information assistance (transit vehicle, rail, compact, air, etc.), shipping, legitimate traffic, measuring of highway speed restrictions, etc. Most subsequent Data Analytics insight is needed for transport and movability industry, ITS implementations, threshold instances, areas and use cases, including the routing, planning, ability to track of infrastructure, platform architecture, and more, are examined in this article. Finally, the paper tackles some open problems in ITS with the use of big data analytics.

AUTOMATICS NUMBER PLATE RECOGNITION USING CONVOLUTION NEURAL NETWORK

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used for security, safety, and also commercial aspects such as parking control access, and legal steps for the red light violation, highway speed detection, and stolen vehicle detection. The license plate of any vehicle contains a number of numeric characters recognized by the computer. Each country in the world has specific characteristics of the license plate. Due to rapid development in the information system field, the previous manual license plate number writing process in the database is replaced by special intelligent device in a real-time environment. Several approaches and techniques are exploited to achieve better systems accuracy and real-time execution. It is a process of recognizing number plates using Optical Character Recognition (OCR) on images. This paper proposes a deep learning-based approach to detect and identify the Indian number plate automatically. It is based on new computer vision algorithms of both number plate detection and character segmentation. The training needs several images to obtain greater accuracy. Initially, we have developed a training set database by training different segmented characters. Several tests were done by varying the Epoch value to observe the change of accuracy. The accuracy is more than 95% that presents an acceptable value compared to related works, which is quite satisfactory and recognizes the blurred number plate.

WITHDRAWN: Reduction of drag in box-type and half streamlined automobile vehicles

Fuel efficiency, mileage, cost and comfortable of the car is the key factors for customer attraction, apart from above features; fuel efficiency plays a major role for the overall attractiveness of car. An Influencing factors for fuel efficiency of cars are based on the performance of internal combustion engines and also on the aerodynamic design of body of the car. In the total, 50% of fuel consumption is obtained by the aerodynamic drag force at highway speeds. The drag force is formed by relative motion between air and vehicle. The objective of this project is to trim down the drag coefficient in sedan and SUV cars by reducing the turbulence at exit of the car and reducing the skin friction drag. In SUV car model, tapered smooth edges are used for the analysis, in sedan car, diffuser is used for the analysis purpose. While designing an automotive, the aerodynamic drag plays a major role. Due to the aerodynamic design, the entire performance of the automotive will be changed. In recent years, the demand for the fuel has been increasing, so extensive research is undergoing for improvement of aerodynamically optimized vehicle designs by using PTC Creo Parametric software and Autodesk Flow Design software. The simulation results are taken under the concept of Computational Fluid Dynamics with necessary boundary conditions.

Impacts of freeway speed limit on operation speed of adjacent arterial roads

Speed limit is one of the essential factors associated with roadway safety. Spillover effect is the tendency of drivers to drive at a higher speed on relatively lower-speed non-freeway roadways after exiting higher-speed freeways. Speed studies were conducted on urban arterial roads with speed limits of 45 mph - all adjacent to freeways with speed limits of 55 mph and 70 mph. A comparison of speed differences between motorists who exited the freeway and those who were already driving on the arterial road was performed. Results showed that the mean speed and 85th percentile of passenger cars on arterials adjacent to 55 mph freeway were 47.3 and 51.1 mph, respectively, while the mean speed and 85th percentile of passenger cars on arterial adjacent to a higher speed 70 mph freeway were 49.5 and 53.4 mph, respectively. Differences in the mean speeds between vehicles exiting from freeways and vehicles already driving on the arterial road were observed at all four sites evaluated. This paper provides researchers, policymakers, and engineers with a better understanding of the effects of speed spillover on adjacent roads when determining whether to increase the speed limit on freeways.

A fuzzy multicriteria method for ranking the factors that influence the settlement of Brazilian highway speed limits

O processo de definição da velocidade limite em rodovias envolve uma série de fatores, não claramente definidos em termos de uma ordem de relevância que possibilite o especialista – responsável por tomar decisão – optar pela melhor escolha. Como o estabelecimento de um limite de velocidade se caracteriza pela subjetividade, sendo um processo regido pela incerteza e pela imprecisão, a lógica fuzzy mostra-se como alternativa para a solução deste problema. Assim, especialistas da área foram questionados quanto à influência de fatores pré-determinados na velocidade limite de rodovias e, em seguida, foi aplicado um método fuzzy multicritério, otimizado por meio de um algoritmo genético, para que as variáveis fossem hierarquizadas em termos de pesos e, consequentemente, relevância. O método convergiu a resultados satisfatórios, possibilitando não somente a concepção de um sistema especialista para o estabelecimento de velocidades limites, mas também aplicações em outras áreas.

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

Connected vehicle real-time traveler information messages for freeway speed harmonization under adverse weather conditions: Trajectory level analysis using driving simulator

This paper employed a high-fidelity driving simulator to investigate the impacts of the Wyoming Department of Transportation (WYDOT) Connected Vehicle (CV) Pilot’s Traveler Information Messages (TIMs) on drivers’ speed selection and the safety benefits of their speed harmonization. Three driving simulator experiment scenarios were developed to simulate the typical traffic and weather conditions on the rural Interstate 80 (I-80) in Wyoming. A total of 25 professional drivers from the WYDOT and trucking industry were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the effects of CV TIMs on their speed selection. The results showed that average speed profiles under CV scenarios were generally lower than under baseline scenarios, particularly for winter conditions (snowy and severe weather). The variance of speed under CV scenarios was found to be significantly lower than the baseline scenarios, indicating that CV TIMs have the potential to harmonize the variations in speed. In addition, for the work zone driving simulator experiment, this research revealed that the mean time-to-collision (TTC) under baseline scenario is approximately 40 % lower than CV scenario, and the mean deceleration to avoid a crash (DRAC) under baseline scenario is approximately 19.3 % higher than CV scenario. These findings suggest that CV TIMs can reduce the risk of crashes. Research findings would provide the WYDOT with early insights into the effectiveness of CV TIMs, which could assist with developing more efficient transportation management strategies under adverse weather conditions.

Automated Vehicle Control at Freeway Lane-drops: a Deep Reinforcement Learning Approach

This study develops an optimal, real-time and adaptive control algorithm for helping a Connected and Automated Vehicle (CAV), navigate a freeway lane-drop site (e.g. work zones). The proposed traffic control strategy is based on the Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, and is designed to determine the driving speed and lane-change maneuvers that would enable the CAV to go through the bottleneck, with the least amount of delay. The DQN RL agent was trained using the microscopic traffic simulator VISSIM, where the learning focused on how the CAV may be able to optimally maneuver the lane drop site while driving as close as possible to the freeway speed limit. VISSIM was also used to compare the performance of the DQN-controlled AV, as opposed to a human-driven vehicle with no intelligent control, in terms of the driving speed or travel time needed to traverse the lane drop site, under a congested, real life-like traffic scenario. The research findings demonstrate the promise of DQN RL in allowing the CAV to intelligently, and optimally navigate, through the lane drop site. Specifically, for the scenario for which the agent was trained, the reduction in the CAV travel time was around 96 percent, compared to the base case. The robustness of the RL agent was further tested on various scenarios different from the training case. For those cases, the mean and standard deviation of the reductions in the travel of the DQN-controlled CAV travel times, compared to the base case, were around 31% and 61%, respectively.

Reshaping Field of View and Resolution with Segmented Reflectors: Bridging the Gap Between Rotating and Solid-State LiDARs.

This paper describes the first simulations and experimental results of a novel segmented Light Detection And Ranging (LiDAR) reflector. Large portions of the rotating LiDAR data are typically discarded due to occlusion or a misplaced field of view (FOV). The proposed reflector solves this problem by reflecting the entire FOV of the rotating LiDAR towards a target. Optical simulation results, using Zemax OpticStudio, suggest that adding a reflector reduces the range of the embedded LiDAR with only %. Furthermore, pattern simulation results show that a radially reshaped FOV can be configured to maximize point cloud density, maximize coverage, or a combination. Here, the maximum density is defined by the number of mirror segments in the reflector. Finally, a prototype was used for validation. Intensity, Euclidean error, and sample standard deviation were evaluated and, except for reduced-intensity values, no significant reduction in the LiDAR’s performance was found. Conversely, the number of usable measurements increased drastically. The mirrors of the reflector give the LiDAR multiple viewpoints to the target. Ultimately, it is argued that this can enhance the object revisit rate, instantaneous resolution, object classification range, and robustness against occlusion and adverse weather conditions. Consequently, the reflector design enables long-range rotating LiDARs to achieve the robust super-resolution needed for autonomous driving at highway speeds.

Why are highway speed limits really justified? An equilibrium speed choice analysis

Speed externalities – the impacts that a driver’s speed choice imposes on others – are usually quoted to justify highway speed regulation in the form of maximum speed limits. However, speed externalities could already be internalized if a pre-existing policy were in place that is related to speed, questioning the traditional rationale supporting speed limits. Given the relationship between speed and fuel consumption and the omnipresence of fuel taxes around the world, it comes as a surprise that pre-existing taxes on motor fuel are completely missing in the debate on the general appropriateness of speed regulation. In this paper we develop an economic equilibrium model of highway speed choice accounting for (i) a market failure – speeding externalities capturing the fact that drivers do not internalize the adverse consequences of speeding on other highway users and the society as a whole; (ii) a famous speed related behavioral anomaly – causing adverse effects drivers impose on themselves by falsely predicting the time saving potential of speed; (iii) a pre-existing fuel tax – closing the wedge between the privately and socially optimal speed. The latter creates a ‘government feedback effect’ reflecting the positive feedback effect of speed on fuel tax revenue and so the broader beneficial fiscal impacts of speed choice ignored by drivers. Numerical computations for Germany indicate a dominant ‘government feedback effect’ ((iii) outweighs (i)) for mean speeds up to around 120 km/h presuming fully variable travel time budgets and beyond that speed level when drivers put (some of the) travel time savings from speeding into more highway traveling. This implies that speed externalities are largely internalized and that the externality rationale in favor of speed regulation does not apply to the majority of drivers. Nevertheless, a moderate speed limit on German highways of 130 km/h would probably not compromise economic efficiency, at least as long as the share of diesel cars is significant. Generally, when drivers are subject to lack of rationality leading to excess speeding, (iii) is not able to outweigh (i). Then, speed limits remain highly warranted provided that appropriate “nudging” is not available. The numerical finding is likely to hold for a large number of European countries too but probably not for North American countries where fuel taxes are considerably lower.

Horizontal Alignment Security Design Theory and Application of Superhighways

In China, the maximum design speed of highways is 120 km/h, which first appeared in the Highway Engineering Technical Standard (Trial) in 1951. However, vehicle performance, road design, and construction technology have been greatly improved over the past 68 years. To adapt to the development demands of highway design speeds above 120 km/h in the future, it is urgent to study superhighway alignment design theory. Therefore, the horizontal alignment security design theory of superhighways was developed in this paper. First, the definition, classification, and construction mode of a superhighway and suitable vehicles of different grades are presented. Then, the lengths of straight lines were limited to reduce driving fatigue. Next, the minimum radii of circular curves were calculated based on driver characteristics and stress analysis of operating vehicles. Finally, the minimum lengths of transition curves were calculated based on the centrifugal acceleration of the operating vehicles, the travel time, and the passenger visual characteristics. The calculation and analysis results show that the superhighway linear features conform to the vehicle operating characteristics, and can ensure the safety of driving.