Road Speed Research Articles

TRAFFIC MANAGEMENT ANALYSIS OF THE MANGGARAI STATION AREA ROAD NETWORK

The Manggarai Station area has an urban function as a residential area which resulted in a very high movement within the region. Manggarai Station is one of the busiest stations and one with the most varied transportation hub, i.e., intermodal railway, TransJakarta, and Airport Trains in Jabodetabek. Manggarai Station is located in the residential area whose existing condition needs to be managed in the form of arrangement of vehicle parking queues with several access to and from the Manggarai station. Vehichle queues at the Manggarai station area are mostly caused by KRL transit passengers who need to change the modes of transportation but with unavailability of waiting areas. This study aims to identify and formulate the inventory problems-conflicts at intersection traffic and evaluate the performance of intersections and road-sections on the road networking at the Manggarai Region. The analytical method was used and based on the 1997 Indonesian Road Capacity Manual. The results of the analysis of the intersection's performance in the existing conditions have a delay of 32 seconds/vehicle and an average road speed of 21.5 km/hour. With the implementation of the transit hub construction recommendation and arrangement of the pelican crossing, the speed increases to 30.5 km/hour. Therefore, in the short term, the traffic engineering management recommendation can be applied to solve the problem of congestion and road user accessibility. Keywords: Traffic Management, Station Area, VCR Ratio

고령운전자 대상 조건부 운전면허제도 도입에 대한 수용성 연구

The urgent need for proactive management and measures for elderly drivers is evident due to the continuous increase in the number of traffic accidents and fatalities involving drivers aged 65 and older. This is further compounded by the advent of the “super-aging era”. However, the measures implemented in South Korea for elderly drivers are limited to the duration of the fitness test period and the driver's license return system.
 Therefore, the aim of this study is to propose methods for expanding the conditional driver's license system by benchmarking advanced international cases. The goal is to enhance both traffic safety and the rights of mobility. Additionally, we suggest introducing a third-party reporting system to quickly identify candidates for occasional fitness tests and to consider imposing a conditional license on them.
 The conditional driver's license system currently being implemented in several countries. It imposes imposing restrictions on including as road limitations, speed limits, and restrictions. currently. Some countries also have a third-party (community) reporting system that allows family members or healthcare professionals to report drivers who pose a high risk. The results of the analysis of traffic accidents revealed that the fatality rate among elderly individuals is higher, especially during nighttime and freeway driving. This highlights the importance of implementing safety policies. Over half of the respondents supported the introduction of the conditional driving license system and believed that additional tests related to driving fitness, including eyesight, are essential.
 It was concluded that the implementation a the conditional driver's license system, similar to those used by advanced nations, is necessary to the safety and protect the minimal rights of elderly drivers' mobility. Most importantly, careful consideration of criteria for conditional licenses, along with the simultaneous introduction of a third-party reporting system, are essential components to enhance the safety of elderly drivers in this regard.

Detection of Lane and Speed Breaker Warning System for Autonomous Vehicles Using Machine Learning Algorithm

Abstract: Road safety holds immense significance in today's world of transportation, primarily focusing on making roads safer to minimize the chances of accidents and injuries. Road safety revolves around creating and maintaining the road infrastructure. Our System enables vehicles to identify and slow down when approaching road bumps or speed breakers, and potholes. It utilizes cameras and sensors to help drivers stay within their designated lanes keeps an eye on lane markings and provides alerts if the vehicle begins to steer out of its lane, promoting safer and more reliable driving experiences. In high-risk areas or near accidentprone intersections, lane detection technology can trigger speed reduction measures such as illuminated signs and flashing lights when vehicles are not within their lanes. The integration of Natural Language processing (NLP), Computer Vision, Machine learning, and Deep learning can create a comprehensive approach to reducing accidents and improving overall road safety.

Applying time-resolved noise maps to assess the impact of road-traffic measures on wake-up reactions in urban environments

The present study investigates the effect of traffic measures in an urban area on the wake-up reactions at nighttime of the residents in the vicinity. These measures include road blocks and speed limits along a few selected streets in a part of Berlin representing the studied area. A temporally resolved noise mapping method based on the CNOSSOS-EU directive is used to calculate the noise level at discrete locations. The number of reactions and the awakening frequency are estimated based on the metrics established in a previous study. The results show a reduction in adverse sleep disturbances leading to awakening due to the traffic restrictions during the night.

Smart Crosswalk: Machine Learning and Image Processing based Pedestrian and Vehicle Monitoring System

The conventional pedestrian crossing system's shortcomings require urgent reform to enhance the safety of pedestrians and improve urban mobility. Issues such as insufficient time for pedestrians to cross, prolong waiting times, neglection of emergency vehicles, and the absence of effective 24/7 response mechanisms at traditional crosswalks present significant safety concerns in urban areas. Our primary intention is to develop a cutting-edge pedestrian crossing system that relies on deep learning and image processing technologies as its foundation. This research addresses to innovate an advanced smart crosswalk consisting of four essential components: a real-time Pedestrian Detection and Priority System customized for individuals with special needs, a responsive system for detecting road conditions, vehicle availability and speed near crosswalks, a real-time Emergency Vehicle Detection and Priority System strengthened by rigorous verification procedures, and a robust framework for identifying pedestrian accidents and violations of crosswalk rules. The entire system has been meticulously designed not only to enhance pedestrian safety by identifying potential dangers but also to optimize traffic flow. In essence, it aims to provide an improved pedestrian crossing experience characterized by increased safety and efficiency.

Towards Sustainable Development in Road Safety: Assessing Public Awareness on Basic Road Traffic Practices in Batu Pahat, Johor

Road accident contributes to high fatality rate around the world and being the 8th leading cause of death of all ages worldwide. Thus, road safety is essential for ensuring the safety and security of individuals of all ages, as well as reducing the number of traffic accidents and injuries as a one way to achieve Sustainable Development Goals 2030. This study aims to assess the level of road safety awareness in terms of basic road traffic practices among public who use Jalan Kluang Federal Route 50 (FT050). The level of knowledge on basic road traffic practices, the level of perceived real practice on basic road traffic practices, and the relationship between their level of knowledge and perceived real practice on basic road traffic practices were investigated. A survey method via questionnaire was distributed randomly to 200 respondents who are adults aged 18 and above and who live around Batu Pahat areas using Google Form online platform. Relevant statistical methods via Statistical Package for the Social Science Software version 26.0 (SPSS) such as descriptive, Mean Score Method, Correlation and Regression analyses were used to analyse the data. It is found that the level of knowledge on basic road traffic practices among public is high. Meanwhile, the level of perceived real practice on basic road traffic practices among public is low. Regression analysis results show that the awareness on knowledge of basic road traffic practices variable (β = 0.624, t = 11.229, p < 0.05) demonstrate a statistically significant relationship with perceived real practices, indicating that, the better an individual’s awareness on knowledge of basic road traffic practices (e.g. importance of safety belt, safety helmet, child restraint system (CRS) and speeding) the better the individual to actually apply the basic road traffic practices (e.g. the use of seat belt, safety helmet, CRS and speeding).

Multimodal Deep Learning for Robust Road Attribute Detection

Automatic inference of missing road attributes (e.g., road type and speed limit) for enriching digital maps has attracted significant research attention in recent years. A number of machine learning-based approaches have been proposed to detect road attributes from GPS traces, dash-cam videos, or satellite images. However, existing solutions mostly focus on a single modality without modeling the correlations among multiple data sources. To bridge this gap, we present a multimodal road attribute detection method, which improves the robustness by performing pixel-level fusion of crowdsourced GPS traces and satellite images. A GPS trace is usually given by a sequence of location, bearing, and speed. To align it with satellite imagery in the spatial domain, we render GPS traces into a sequence of multi-channel images that simultaneously capture the global distribution of the GPS points, the local distribution of vehicles’ moving directions and speeds, and their temporal changes over time, at each pixel. Unlike previous GPS-based road feature extraction methods, our proposed GPS rendering does not require map matching in the data preprocessing step. Moreover, our multimodal solution addresses single-modal challenges such as occlusions in satellite images and data sparsity in GPS traces by learning the pixel-wise correspondences among different data sources. On top of this, we observe that geographic objects and their attributes in the map are not isolated but correlated with each other. Thus, if a road is partially labeled, then the existing information can be of great help on inferring the missing attributes. To fully use the existing information, we extend our model and discuss the possibilities for further performance improvement when partially labeled map data is available. Extensive experiments have been conducted on two real-world datasets in Singapore and Jakarta. Compared with previous work, our method is able to improve the detection accuracy on road attributes by a large margin.

Predicting and factor analysis of rider injury severity in two-wheeled motorcycle and vehicle crash accidents based on an interpretable machine learning framework

Objective As one of the vulnerable road users in accidents, how to improve the two-wheeled motorcyclist’s driving safety and reduce accident injury is a public health issue. Accurate identification of the factors influencing the severity of accidents is an important prerequisite for mitigating injury from crashes. Methods Based on a vehicle and a two-wheeled motorcycle crash accident data from the China in-depth accident study database (CIDAS), this study uses the performance evaluation indicators of accuracy, precision, recall, F1-score, AUC, and the ROC curve. The classification and prediction performances of the six machine learning methods on the dataset are compared, and the LightGBM algorithm with the best performance is selected to model the accident injury severity of the motorcyclists. The SHAP method is used to extend the interpretability of the LightGBM model results. Based on the SHAP method, the importance, main effect, and the interaction effect of factors under each accident injury severity are quantitatively analyzed. Results The model prediction accuracy is 92.6%, the F1-Score is 92.8%, and the AUC value is 0.986. The importance of factors varies with the accident injury severity of motorcyclists. The kilometers traveled per year by the driver, the throwing distance of the motorcyclist, and the road speed limit are the three most important factors. The motorcyclist is more likely to suffer fatal injuries when the throwing distance is >1,000 cm. Conclusions The prediction model of driver injury severity based on LightGBM algorithm has a good prediction performance. It can be used to analyze the influence factors of injury severity in two-wheeled motorcyclist accident by combining the model with SHAP method. These results could help the traffic management department to take measures to reduce accident injury of motorcyclists.

87 Virtual Driving Relates to Real-World Risky Driving

Objective:Driving is a cognitively demanding activity commonly affected by brain injury and illness. Accurate driving assessment is essential for reducing risk, optimizing independence, and informing driving-related interventions. Virtual reality driving simulation (VRDS) enables safe, sensitive, objective, and standardized measurement of driving abilities. VRDS has been validated in relation to self-reports and driver records. However, self-reports are subjective, and driver records include only major events (collisions, violations). Video telematics platforms can measure naturalistic driving in a more objective and sensitive manner. The present study used video telematics to examine relationships between VRDS performance and directly observed naturalistic driving.Participants and Methods:20 healthy adult drivers (ages 23-61, mean age=36; 75% women) completed a VRDS assessment that included 1) driving on a straight road, 2) following a truck on a highway, and 3) reacting to a child running into a street to retrieve a ball. Primary VRDS measures were 1) speed and lane management on the straight road; 2) speed and following distance management in the truck-following task; and 3) reaction time, stopping, and distance from the child in the child-ball task. Participants also completed 28 days of naturalistic driving with a video telematics platform in their vehicle. Driving events were detected automatically using accelerometer, GPS, and video data, and driving behaviors were coded by driving risk analysts. The primary naturalistic measure was the number of unsafe driving behaviors per hour driven; specific driving behaviors served as exploratory variables. We examined correlations between VRDS and naturalistic driving variables. Given limited statistical power, we reported correlations that were small-to-medium or greater (r>.2) in primary analyses and medium-to-large or greater (r>.4) in exploratory analyses.Results:On average, drivers exhibited approximately one unsafe driving behavior per hour (M=0.9, SD=0.9, range=0.1-2.7). Common behaviors were failing to stop, unsafe following distance, speeding, and cell phone use. No collisions occurred. Average lane position in VRDS (specifically, leftward deviation from the center of the lane) was correlated with more real-world unsafe driving behaviors per hour (r=.35, p=.13), as were higher average straight road speed (r=.26, p=.27), greater straight road speed variability (r=.28, p=.24), and failing to stop for the child in the child-ball task (r=.22, p=.36). In exploratory analyses, failing to stop for the child was associated with real-world distracted driving (r=.45, p=.047), greater lane position variability in VRDS was associated with real-world unsafe following distance (r=.57, p=.009), and greater speed variability in VRDS was associated with real-world seat belt non-use/misuse (r=.49, p=.03).Conclusions:The present findings provide preliminary evidence that VRDS variables are related to directly observed naturalistic driving, supporting the potential utility of VRDS as a sensitive, ecologically valid driving evaluation tool. As the present study used a small sample of healthy drivers, further research will explore this topic in larger samples and in clinical populations, such as acquired brain injury. Future work will also investigate whether incorporating VRDS with conventional driving evaluation tools (e.g., neuropsychological tests, behind-the-wheel assessments) can enhance the ability of clinical driving evaluations to predict real-world risky driving.

Traffic Volume Forecast of Parking Lot Entrance and Exit Roads

As people's living standards improve, commercial plaza development also more and more quickly, commercial plaza and the surrounding areas of transport vehicles, in the parking lot entrances and exits of highway road traffic conditions, the parking lot entrances and exits of low traffic capacity and traffic organization chaos (motor vehicles, electric vehicles and the status of the bicycle traffic complex). It is necessary to predict the traffic volume of the entrance and exit roads of the parking lot. In ningyang county lingyun building as an example, through direct observation records investigation parking lot parking condition and facilities, analysis of traffic organization form of the parking lot entrances, investigate the status of the parking lot entrance road and surrounding roads, using artificial paper counting method to collect parking entrance road traffic flow, speed and other data, The time series moving average and exponential smoothing are used to predict the traffic volume at the entrance and exit of the parking lot. The prediction results show that the two methods are suitable for the parking lot entrance traffic volume prediction, and the moving average method is more accurate than the exponential smoothing method, which is closer to the actual value.

City-scale Vehicle Trajectory Data from Traffic Camera Videos

Vehicle trajectory data underpins various applications in intelligent transportation systems, such as traffic surveillance, traffic prediction, and traffic control. Traditional vehicle trajectory datasets, recorded by GPS devices or single cameras, are often biased towards specific vehicles (e.g., taxis) or incomplete (typically < 1 km), limiting their reliability for downstream applications. With the widespread deployment of traffic cameras across the city road network, we have the opportunity to capture all vehicles passing by. By collecting city-scale traffic camera video data, we apply a trajectory recovery framework that identifies vehicles across all cameras and reconstructs their paths in between. Leveraging this approach, we are the first to release a comprehensive vehicle trajectory dataset that covers almost full-amount of city vehicle trajectories, with approximately 5 million trajectories recovered from over 3000 traffic cameras in two metropolises. To assess the quality and quantity of this dataset, we evaluate the recovery methods, visualize specific cases, and compare the results with external road speed and flow statistics. The results demonstrate the consistency and reliability of the released trajectories. This dataset holds great promise for research in areas such as unveiling traffic dynamics, traffic network resilience assessment, and traffic network planning.

Real-Time Implementation of Sensorless DTC-SVM Applied to 4WDEV Using the MRAS Estimator

This article presents the DTC-SVM approach for controlling a sensorless speed induction motor. To implement this approach, a practical prototype is built using a microcontroller, an embedded GPS module, and a memory card to collect real-time data during the driving route, such as road geographical data, speed, and time. These data are then utilized in the laboratory to implement the control law (DTC-SVM) on the electric vehicle. The d-q model of the induction motor is first presented to explain the requirements for calculating the rotor speed. Then, an adaptive model reference system speed estimator is developed based on the rotor flux, along with a controller and DTC-SVM strategy, which are implemented using the dSpace 1104 board to achieve the desired performance. The simulation results demonstrate satisfactory speed regulation with the proposed system. In this study too, an electronic differential system is modeled for the four wheels of an electric vehicle equipped with an integrated motor, all controlled by the DTC-SVM strategy. Vehicle speed and electrical vehicle steering angle variations, as well as wheel speeds estimated by code system, are verified using MATLAB/Simulink simulations.

Real-Time Predictive Energy-Saving Control for Electric Vehicle Based on Road Slope Prediction

Predictive energy-saving control (PEC) is aimed at reducing energy consumption by designing the vehicle speed while considering future road and traffic information. In particular, the slope of the road ahead is necessary and critical for PEC. This paper proposes a road slope prediction method for production vehicles that uses the nonlinear autoregressive (NAR) neural network model based on road slope sensors. To adaptively balance the energy savings and trip time, this paper proposed a real-time variable weight PEC method for a four-wheel-drive (4WD) intelligent electric vehicle. The weight coefficients are automatically changed according to the characteristics of the road slope, where the vehicle energy-saving rate on the steep downhill road can be maximized. The results of real-time simulation on the dSPACE platform indicated that the road slope predictive model can be run in real time and adapted to changes in road slope and speed. The root mean square error (RMSE) of the predictive results is 0.3063. On a steep downhill road, the energy-saving rate of the proposed PEC method can reach 30.87% at a small expense of time of 3.75%. On uphill and flat roads, energy can be saved by 6.35% at a time cost of 3.0%. Compared with the PEC with constant weight factors, the two control objectives of energy savings and traveling time can be better balanced on various types of roads.

A New Traffic System on Driver Sensitivity and Safe Distance Headway

A new macroscopic traffic system is devised that observes the transition distance between the vehicles and driver sensitivity during traffic evolution. The driver sensitivity in this system is based on the traversed time over a 200 m road section and speed (velocity). In addition, the proposed system considers the safe distance headway as the distance between vehicles changes. An analogy system for vehicle flow behavior is devised from a spring–mass system with changes in traffic. The proposed system can characterize traffic evolution for small and large changes in density. Furthermore, the changes in the travel of traffic rearwards during congestion and forward during smooth flow are dependent on driver sensitivity, transition distance, and safe distance headway. The proposed traffic system is hyperbolic. The Payne Whitham traffic system is based on uniform constant velocity for different conditions, which characterizes traffic evolution unrealistically. The proposed traffic system and the Payne Whitham system are assessed over a 2000 m circular road for large changes in density in two examples. Both the Payne Whitham and proposed traffic systems are numerically implemented with the first order centered scheme in Matlab. The discretization stability of both systems is enforced with the Courant–Friedrich–Levy (CFL) condition. The proposed system with lower driver sensitivity evolves with larger changes, whereas the proposed system with larger density has smaller changes in density and velocity. The simulation results showed that the traffic evolution with the proposed system is more appropriate than with the Payne Whitham system.

Investigating the influential factors in real-world energy consumption of battery electric vehicles

The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policymakers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.

To share or not to share? Revealing determinants of individuals’ willingness to share rides through a big data approach

Ridesplitting has been widely recognised as a promising mobility mode for sustainable transportation, but its success largely depends on a sufficient number of passengers who are willing to share their rides. To uncover the determinants of willingness to share (WTS), prior studies typically relied on either individual-level survey-based or aggregate-level data-driven methods. To combine the former’s strength in capturing individual choice preferences and the latter’s advantage in utilising available multi-source big data, this study proposes a big data approach to modelling individual choices between the solo and shared options for each trip. To reconstruct the choice process, we leverage large-scale real-world trip records and propose a learning framework to not only retrieve the trip time and fare of the chosen option (solo or shared), but also impute the likely time and fare of the alternative option. These reconstructed trip attributes are then integrated with the sociodemographic, built environment and traffic features from other data sources. Finally, all these variables are fed into a random coefficient logit model to reveal passengers’ ridesplitting preferences. Through a case study of Manhattan, New York City, we reveal the spatiotemporal pattern of WTS and its determinants. Results show that WTS varies greatly across space and time. The time-fare trade-off is identified as the most essential factor, with the value of time revealed to be about $28-36/h. WTS decreases with longer trip distance/commuting time/distance to the urban centre, lower road speed, and higher speed fluctuation/bus station/crime density, but increases with a higher proportion of middle-class/female/young residents, residential land use and metro station. The proposed methodology can be used to explain and monitor WTS in a cost-effective way, complementing traditional survey-based methods to better design and promote ridesplitting services.

Adaptive Dynamic Path Planning Method for Autonomous Vehicle Under Various Road Friction and Speeds

Path planning is a crucial technology for autonomous vehicle (AV). However, it is difficult to adapt to dynamic driving environment, and AV may lose lateral dynamic stability due to high speed and various friction. This paper presents an adaptive dynamic path planning method (ADPPM) for AV to address the challenges. The ADPPM is comprised of three components: 1) A dynamic state-fused steering decision method based on a hierarchical fuzzy inference system is designed to calculate the steering position of AV in each iteration step, and the method fuses the multi-state of the dynamic environment; 2) dynamic path optimization method is designed to reduce the mean curvature of the path based on the particle swarm optimization method, which improves the lateral dynamics stability of AV; 3) adaptive speed inference method is proposed to provide desired steering speed for AV according to various road friction and reduce AV’s steering burden. The ADPPM provides path planning in the dynamic environment, and it also improves the stability of AV under various road friction and speeds. Finally, the proposed method is verified by CarSim.

When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time

Predicting how long a trip will take may allow travelers plan ahead, save money, and avoid traffic congestion. The journey time estimation model should take into account three crucial factors: (1) individual travel preference, (2) dynamic spatio-temporal correlations, and (3) the association between long-term speed forecast and travel time estimate. In order to overcome these challenges, this study proposes a unique parallel architecture called the multi-task spatio-temporal attention network (MT-STAN) to estimate journey times. To extract the dynamic spatio-temporal correlations of the road network, we first develop a traffic speed prediction model based on spatio-temporal block and bridge transformer networks, combining the road, timestamp, and traffic speed information into hidden states. Second, we offer a personalized model for estimating journey times that makes use of cross-network, holistic attention, and semantic transformer. In this approach, travel preferences extraction through cross-network, holistic attention permits correlations between the dynamic road network’s hidden states and individual journey characteristics, which are subsequently transformed into global semantics by the semantic transformer; preferences and semantics are integrated during the estimate phase. Finally, a multi-task learning component is included, which combines both traffic speed prediction and individual journey time estimate, via the sharing of underlying network parameters and the improvement of the contextual semantic knowledge of the latter job. Evaluation experiments are carried out using a highway dataset collected in Yinchuan City, Ningxia Province, China. The proposed prediction model outperforms state-of-the-art baseline approaches in experiments.

US road safety during COVID-19: motorist, pedestrian and bicyclist fatality trends

Purpose The purpose of this paper is to illustrate how COVID-19 lockdowns in the USA impacted traffic safety. Design/methodology/approach The authors explored the role of vehicle, user and built environment factors on traffic fatalities in the USA, comparing results during COVID-19 lockdowns (March 19th through April 30th, 2020) to results for the same time period during the five preceding years. The authors accomplished this through proportional comparisons and negative binomial regression models. Findings While traffic levels were 30%–50% below normal during the COVID-19 lockdowns, all traffic fatalities decreased by 18.3%, pedestrian fatalities decreased by 19.0% and bicyclist fatalities increased by 3.6%. Fatal COVID-19 crashes were more likely single-vehicle crashes involving fixed objects or rollovers. COVID-19 traffic fatalities were most common on arterial roadways and in lower density suburban built environments. Findings suggest the importance of vulnerable road users, speed management and holistic built environment policy when pursuing safety on the streets. Originality/value The findings have road safety implications not only for future pandemics and other similar events where we would expect decreases in motor vehicle volumes (such as natural disasters and economic downturns) but also for cities that are pursuing mode shift away from personal automobiles and toward alternative modes of transportation.

Dynamic analysis of an electro-hydraulic interconnected actuator energy regeneration suspension

A hydraulic interconnected suspension with energy recovery (EHA-HIS) is proposed to enhance riding comfort and road-holding ability while converting vibration energy into usable electrical energy. This paper mainly focuses on the dynamic analysis of a vehicle suspension model integrated with the EHA-HIS. The mathematical model of EHA-HIS is established based on the system structure analysis. The characteristics of the EHA-HIS are given based on a parameter analysis including the input and structural parameters under a sinusoidal excitation. Thereafter, vehicles equipped with the EHA-HIS and traditional suspensions are compared in terms of dynamic performance, and the simulation is implemented to study the energy harvesting performance of a vehicle with the EHA-HIS. The simulation results suggest that the damping force and harvested power are proportional to the frequency and amplitude of the sinusoidal input. The simulation also shows that the piston diameter has the greatest influence on the characteristics of the EHA-HIS. The analysis indicates that EHA-HIS can recover vibration energy while improving vehicle dynamic performance, and the harvestable power increases with the road grade and vehicle speed.