Vehicle Length Research Articles

Analisis Pengaruh Pejalan Kaki Yang Menyeberang Jalan Pada Fasilitas Penyeberangan Zebra Cross Terhadap Panjang Antrian Kendaraan

The increase number of vehicles is not the main factor causing congestion. There are other factors, such as the movement of pedestrians crossing the road using a zebra cross. This study aims to determine the effect of pedestrians crossing on vehicle queue lengths using shock wave analysis, and evaluate whether zebra cross facilities are still suitable for use or not at research site. This research was conducted for 4 days on Sam Ratulangi street in front of Multimart Ranotana Manado City. Data analysis uses traffic characteristics correlation of volume, speed and density. The characteristic correlation that will be used for the shock wave analysis calculation is based on the highest coefficient of determination (R2). The results of the study, based on shock wave analysis showed that pedestrians crossing could make a vehicle queue length of 33.5 meters for the left lane and 74.6 meters for the right lane. Based on the average number of pedestrians crossing the volume of vehicles and the number of conflicts that occur (PV2), it is recommended that the type of crossing facility is pelican with a protector. The greater duration of pedestrians crossing (r) then the maximum queue length of vehicles (Qm) will be greater.

Effect of the geometrical parameters on the vehicle's moving stability

To enhance the moving stability and safety of vehicles, kinematics and dynamics model of the vehicle consisting of a guided front wheel and a free rear wheel is established based on the model of a four-wheeled vehicle when the vehicle moves on the curve-road. The effect of the geometrical parameters and dynamics parameters including the factor of the moving stability, vehicle moving speed, lateral stiffness parameters of the front and rear wheels, vehicle mass, vehicle length on the vehicle moving stability and safety is then simulated and analyzed, respectively. The research results indicate that the operating parameters of the vehicle greatly affect the vehicle moving stability. The lateral stiffness parameters of the front and rear wheels should be increased while the vehicle mass needs to be reduced in the operating condition of the vehicle to enhance the vehicle moving stability. Besides, the vehicle length L should be reduced and the longitudinal distance of a needs to be distributed by 0.95×a to further improve the vehicle moving stability.

Analytical Solution of the Mixed Traffic Flow Cellular Automaton FI Model with the Next-Nearest-Neighbor Interaction

Based on a one-dimensional (1D) traffic flow cellular automaton (CA) FI model, a deterministic next-nearest-neighbor interaction FI model (NIFI model) is proposed. Using the mean-field analysis, the analytical solution of the NIFI model in one-dimensional traffic flow is derived under periodic boundary conditions. For the mixed traffic flow, the occupancy and the mixing ratio are introduced to describe the mixing effect. Similarly, using the mean-field method, the exact solution of the mixed traffic flow is derived from the long-time evolution to reach the steady state. The numerical simulations are carried out for the mixed traffic flow with different vehicle lengths, maximum velocities, and mixing ratios to verify the analytical solutions. The results show that the numerical simulation results agree well with the analytical solution.

Innovative Method for Estimating Large Truck Volume Using Aggregate Volume and Occupancy Data Incorporating Empirical Knowledge Into Linear Programming

Monitoring large trucks (LT) plays an essential role in the operation and planning of transportation and economics. Although many “advanced” traffic detectors (e.g., infrared, microwave, and magnetic sensors) can identify LT, those technologies are not as widely deployed as loop detectors, and their performance is disputed. While many previous studies have investigated estimation of truck traffic using gross volume and occupancy information from loop detectors, most of them are based on mean effective vehicle length (MEVL), and thereby vulnerable to dynamic traffic conditions and dependence on exogenous data sources. This paper aims to develop an original method of estimating LT volume using detector output (i.e., volume and occupancy). A linear optimization problem is formulated by minimizing the difference between observed occupancy and theoretical “best guess.” LT and short vehicle (SV) lengths and their counts are devised as decision variables to compensate for variations in speed and length. To assess the proposed method, volume and occupancy data output in 30 s granularity from three different cases are prepared: microscopic simulation, Tennessee Radar Detector System (TN-RDS), and California Freeway Performance Measures System (PeMS). For the simulation, 12 different scenarios were built up and the results indicate that the mean absolute error of LT estimates increases with traffic volume and truck penetration. In two real-world cases, the LT estimates are close to ground truth from video and weigh-in-motion (WIM) station records. Furthermore, this simple and online LT volume estimate approach can be extended to other stations and to monitor a wide range of LT activities.

Data-Driven Computation of State-Dependent Passenger Car Equivalency for Multiple Truck Lengths

Passenger car equivalency (PCE) has been used for decades to convert mixed vehicle traffic into equivalent pure passenger car traffic streams for transportation planning, design, and operational analysis purposes. The term PCE was defined by the Highway Capacity Manual (HCM) in 1965 as an adjustment factor, and many approaches have continued to be developed for refining PCE values for different types of freeway geometry and vehicle distributions since then. This paper proposes an improved data-driven state-dependent passenger car equivalency (PCE–SD) approach, initially for level terrain basic freeway segments that consider average vehicle lengths for different types of vehicles. The term “state-dependent” refers to the space headway ratio at the same speed ranges between long vehicles (trucks), that fall into the same clustered category, and passenger cars. The relatively unique data stream used in this study comes from the Portland Oregon Regional Transportation Archive Listing (PORTAL) online datahub. This rich continuous data stream provides valuable information about traffic-related data at a 20-s aggregation level. Most importantly for this effort, ranges of categorical vehicle lengths are also available. According to analysis results, PCE–SD values estimate the actual passenger car distribution at different speeds and levels of operational performance (levels of service) more accurately compared with the HCM-6 PCEs on level terrain basic freeway segments. The authors propose that a state-dependent passenger car equivalency approach bridges the conversion gap from different types of trucks to passenger vehicles and could be used as an improved supplementary methodology.

Vehicle Intelligent Classification Based on Big Multimodal Data Analysis and Sparrow Search Optimization.

Vehicle intelligent classification plays a vital role in the Intelligent Transport Systems. However, due to the dynamic traffic environments, it is difficult to ensure the classification accuracy. Therefore, this article uses a new pulse coherent radar (PCR) to collect road vehicle data, and a vehicle classification method of sparrow search algorithm extreme learning machine (SSA-ELM) based on big multimodal data analysis is proposed. First, the road vehicle data are collected by PCR, where the vehicle length, chassis outline, and height features are extracted as the sample data. Then, the ELM is utilized to learn these three modal features. According to the input feature data, the vehicle type is classified, including cars, sport-utility vehicles, and buses. Finally, the SSA is applied to optimize the initial weights and thresholds of ELM. Experimental results show that SSA-ELM has notable advantages in classification accuracy and convergence speed, compared with existing benchmark methods.

Decision of Queuing Models and Layout Design at a Gas Station

Objectives: This study aims to analyze the accuracy of the queuing models and the application of layout design at the gas station in Ngasinan Wonosobo, Indonesia. Fuel service providers are encouraged to optimize service space as the number of motorized vehicle customers grows. The focus of attention in gas station management is not only on the queuing model, but also on the layout design that is acceptable and adequate for customers, particularly motorcycle riders who use Pertalite. This study will look at how to simulate a realistic gas station queue and provide a user-friendly layout design for anyone interested in gas stations.Methodology: The object of this study was a motorcyclist who was doing Pertalite refueling. The types of data used are quantitative and qualitative data, so this study is classified as mixed methods. Purposive sampling was utilized as the sampling method in this study, which is a non-probability sampling strategy. The analytical method used is descriptive analysis and analysis of the Multi-channel-Single phase (M/M/S) queuing model.Finding: The findings of this study indicate that long queues can interfere with the activities of other facility users for other customers at the gas station. This study identifies the need for a neat layout design for motorcycle queues by utilizing the service-scape gas station.Conclusion: Although the queue system performs well in the afternoon, the results of the queuing analysis show that the total length of vehicles in the system does not exceed the length of the line, ensuring that the queue does not obstruct other vehicle paths. Recommendations and study findings are explored as a queuing model and layout design in the future.

Women-only parking spaces: Determinants of parking space choice, investment decision, and a case study of Wuhan, China

This paper investigates the effects of introducing women-only parking spaces on drivers’ parking behavior and operator’s investment behavior based on a field survey of a parking lot in Wuhan, China. An econometric model is presented to identify the major determinants of influencing drivers’ time for completing the parking process at a parking space and their parking choice decisions towards the women-only parking spaces. Optimization models are developed to jointly determine the optimal proportion of the women-only parking spaces in a parking lot and/or the optimal parking fees so as to maximize the total net profit of operator, which is from the operations of car park and shopping mall. A case study from Wuhan China is conducted for illustrating the applications of the models. The findings show that driver’s gender, driving age, vehicle length, and parking environment (obstacles on one or both sides of a parking space) have significant impacts on the time for completing the parking process at a parking space. Women-only parking spaces can help reduce such time. Driver’s gender, driving age, and the past parking experience at the women-only parking spaces significantly influence driver’s choice of the women-only parking spaces, whereas educational degree and the past parking experience have no statistically significanteffects. However, investment in the women-only parking spaces may lead to a loss in the net profit of operator, and thus the investment decision on the women-only parking spaces should be carefully made for the operator in practice. These findings have important implications for planning of parking facilities and evaluation of parking policies.

Scramjet Performance Computed for a JP-7-Fueled Generic X-51 Vehicle

Ways to improve the performance of a dual-mode scramjet engine within a generic X-51 hypersonic vehicle were computed using the MASIV reduced-order model. Two new aspects are that the finite-rate chemistry of a hydrocarbon (JP-7) fuel was added for the first time, and certain tradeoffs were quantified that arise due to the short vehicle length (4.6 m), which narrows the operating ranges of ram and scram modes. A goal was to maximize the operating range of the ram mode, which is limited by unstart, as well as the range of the scram mode, which is limited by incomplete fuel burn. Four parameters were varied: fuel–air equivalence ratio, Mach number, fuel injector diameter, and number of fuel injectors. Results indicate that achieving an acceptable design is complicated by the mixing–reaction–strain out tradeoff. A solution was achieved by directing the fuel to two sets of wall injectors having small diameters for ram mode and larger diameters for scram mode. Results demonstrate the need to include finite-rate chemistry to properly compute the heat release profile and interactions between the inlet, isolator, combustor, and thermal choking.

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions.

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

SenseMag: Enabling Low-Cost Traffic Monitoring Using Noninvasive Magnetic Sensing

The operation and management of intelligent transportation systems (ITS), such as traffic monitoring, relies on real-time data aggregation of vehicular traffic information, including vehicular types (e.g., cars, trucks, and buses), in the critical roads and highways. While traditional approaches based on vehicular-embedded GPS sensors or camera networks would either invade drivers' privacy or require high deployment cost, this paper introduces a low-cost method, namely SenseMag, to recognize the vehicular type using a pair of non-invasive magnetic sensors deployed on the straight road section. SenseMag filters out noises and segments received magnetic signals by the exact time points that the vehicle arrives or departs from every sensor node. Further, SenseMag adopts a hierarchical recognition model to first estimate the speed/velocity, then identify the length of vehicle using the predicted speed, sampling cycles, and the distance between the sensor nodes. With the vehicle length identified and the temporal/spectral features extracted from the magnetic signals, SenseMag classify the types of vehicles accordingly. Some semi-automated learning techniques have been adopted for the design of filters, features, and the choice of hyper-parameters. Extensive experiment based on real-word field deployment (on the highways in Shenzhen, China) shows that SenseMag significantly outperforms the existing methods in both classification accuracy and the granularity of vehicle types (i.e., 7 types by SenseMag versus 4 types by the existing work in comparisons). To be specific, our field experiment results validate that SenseMag is with at least $90\%$ vehicle type classification accuracy and less than 5\% vehicle length classification error.

Efficient Video-based Vehicle Queue Length Estimation using Computer Vision and Deep Learning for an Urban Traffic Scenario

In the Intelligent Transportation System (ITS) realm, queue length estimation is one of an essential yet a challenging task. Queue lengths are important for determining traffic density in traffic lanes so that possible congestion in any lane can be minimized. Smart roadside sensors such as loop detectors, radars and pneumatic road tubes etc. are promising for such tasks though they have a very high installation and maintenance cost. Large scale deployment of surveillance cameras have shown a great potential in the collection of vehicular data in a flexible way and are also cost effective. Similarly, vision-based sensors can be used independently or if required can also augment the functionality of other roadside sensors to effectively process queue length at prescribed traffic lanes. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The queue length is estimated based on count of total vehicles waiting on a signal. The proposed approach calculates queue length without the knowledge of any onsite camera calibration information. Average vehicle length is approximated to be 5 m. This caters for the vehicles at the far end of the traffic lane that appear smaller in the camera view. Identification of stopped vehicles is done using Deep SORT based object tracking. Due to robust and accurate CNN-based detection and tracking, the queue length estimated by using only the cameras has been very effective. This mostly eliminates the need for fusion with any roadside or in-vehicle sensors. A detailed comparative analysis of vehicle detection models including YOLOv3, YOLOv4, YOLOv5, SSD, ResNet101, and InceptionV3 was performed. Based on this analysis, YOLOv4 was selected as a baseline model for queue length estimation. Using the pre-trained 80-classes YOLOv4 model, an overall accuracy of 73% and 88% was achieved for vehicle count and vehicle count-based queue length estimation, respectively. After fine-tuning of model and narrowing the output classes to vehicle class only, an average accuracy of 83% and 93% was achieved, respectively. This shows the efficiency and robustness of the proposed approach.

Probability distribution and recommended values of passenger car external dimensions in mechanical parking garage design

ABSTRACT In order to determine the external dimensions of passenger car, to meet the current parking needs when designing a mechanical parking garage. In this study, 7193 groups of external dimensions and sales volume data of passenger car in the Chinese market from 2009 to 2020 are collected. Then, appropriate probability distributions are used to fit the collected data. Combined with their actual market share, the recommended external dimensions of sedan, SUV and MPV, separately, as well as all of them together, defined as general vehicles, are given by using probability statistics method. It is concluded that the external dimensions of sedan and SUV obey the t Location-Scale distribution, while MPV follows the mixed normal distribution. The length and width of general vehicles follow the t Location-Scale distribution and the height obeys the mixed normal distribution. The length, width and height of sedan are 5100 mm, 1890 mm and 1560 mm, respectively. The ones of SUV are 4950 mm, 1940 mm and 1850 mm, respectively, and for MPV they are 5300 mm, 1900 mm and 2000 mm, respectively. The corresponding dimensions of the general vehicles are 5150 mm, 1930 mm and 1900 mm, respectively. The proposed passenger car external dimensions can provide guidance for the garage design, aiming at the optimum space exploitation.

The design parameters improvement of wide-grip sprinkler machines of circular action

During the process of irrigation of wide-coverage sprinklers (WS) by the interaction of the wheels with the soil, the soil is pressed. In this case, the WS wheels impact the soil with a certain specific pressure. Specific pressure depends on a number of factors such as the length of vehicle, span length, vehicle weight, the diameter of water line, wheel contact area, determined by wheel geometry, pressure and tire type. The article carries out theoretical investigations determining the specific pressure of the wheel on the soil. It also given some recommendations connected with a number of wheels to be installed on the WS body compared with the calculated specific pressure of the designed vehicle with the standard specific pressure.

AN ANALYSIS OF TYPE I DILEMMA ZONE AT SIGNALISED INTERSECTIONS

Intersections, for vehicles coming from different directions, are conflict points in road networks. When a driver approaching a signalised intersection encounters the yellow light, he/she is in a dilemma either to safely stop or to pass through the intersection during clearance time. The decision to stop or to pass may change depending on some factors such as duration of yellow light, deceleration and acceleration rate, width of intersection, speed and length of vehicle, etc. This study aims to put forth the effects of some related factors affecting the length of the Type I dilemma zone. To perform this study, five factors including vehicle speed, maximum deceleration rate, perception-reaction time, clearance time, the total intersection width-vehicle length were considered and a total of 648 different traffic cases were investigated. The study results showed that the Type I dilemma zone length increased with the increase of speed, total intersection width-vehicle length and perception-reaction time, but decreased with the increase of clearance time and deceleration rate.

SYSTEMATIC ANALYSIS OF CHANGING VEHICLE EXTERIOR DIMENSIONS AND RELEVANT VEHICLE PROPORTIONS

AbstractFor the user perception of vehicles and consequently the success of automobile design, the body layout and especially the proportions of vehicles are pivotal. In the present study, the SAE main dimensions length (L108), wheelbase (L101), height (H100) and width (W103) of 697 vehicles from different OEMs were examined. The analysis reveals a tendency of increasing length, wheelbase and especially width of vehicles in recent years. Regarding the ratios of these dimensions there are clear correlations, as revealed by an analysis with linear regression. We find particular strong synchrony in the increases of the pairs W103 - L108 and L101 - L108 and lesser so between the ratio E = L108/L101 and the vehicle length. The latter can be explained by vehicles with a high L108 being less compact, i.e. the proportion E increases. Finally, it is discussed whether technological changes lead to revolutionary or evolutionary changes in dimensions. The historical development and the moderate proportional changes of BEVs indicate a measurable but evolutionary development. In contrast, disruptive technologies such as automated driving can significantly change the vehicle layout and thus encourage a more revolutionary change in proportions.

Estimating Vehicle Speed from Traffic Count and Occupancy Data

Automatic vehicle detectors are now common on road systems across the world. Many of these detectors are based on single inductive loops, from which data on traffic volumes (i.e. vehicle counts) and occupancy (i.e. proportion of time during which the loop is occupied) are available for 20 or 30 second observational periods. However, for the purposes of traffic management it is frequently useful to have data on (mean) vehicle speeds, but this is not directly available from single loop detectors. While detector occupancy is related in a simple fashion to vehicle speed and length, the latter variable is not measured on the vehicles that pass.In this paper a new method for speed estimation from traffic count and occupancy data is proposed. By assuming a simple random walk model for successive vehicle speeds an MCMC approach to speed estimation can be applied, in which missing vehicle lengths are sampled from an exogenous data set. Unlike earlier estimation methods, measurement error in occupancy data is explicitly modelled. The proposed methodology is applied to traffic flow data from Interstate 5 near Seattle, during a weekday morning. The efficacy of the estimation scheme is examined by comparing the estimates with independently collected vehicle speed data. The results are encouraging.

MODELING OF QUEUE LENGTH AT RAILWAY LEVEL CROSSING CLOSE TO A SIGNALIZED INTERSECTION (CASE STUDY: JAGIR WONOKROMO SURABAYA RAILWAY LEVEL CROSSING)

In Indonesia, there are still many level crossings, namely between railways and highways. This will cause congestion, in the form of queue length of vehicles that stop due to the gate closing at the railway level crossing. In addition, level railway crossing located near a signalized intersection can also affect the performance of signalized intersections. This is due to the queue length of vehicle that can interfere with intersection. Based on the problem, it is necessary to build two models. The first model is between the gate closing time at level crossing with the length and speed of the train using multiple regression method. The second model is between the gate closing time at level crossing and the queue length of vehicles using a simple regression method. The result of first model shows that the gate closing time at level crossing has a positive relationship with the length of train and a negative relationship with the speed of train. The relationship among these three variabels is y = -2.083 + 0.637 + 249.930. Y is described as the gate closing time at level crossing, while x 1 is the speed of train and x 2 is the length of train. While the result of second model regarding the relationship between the gate closing time at level crossing with the queue length of vehicles has a positive effect in each direction. The relationship between two variabels is y = 1.2329x + 84.725 for East Jagir Wonokromo Street from east to west direction and y = 0.5176x + 24.44 for East Jagir Wonokromo Street from west to east direction. Y is described as the gate closing time at level crossing and xis described as the queue length of vehicles.

An Empirical Tire-Wear Model for Heavy-Goods Vehicles

ABSTRACT Tire selection has an important impact on the operational costs of heavy-goods vehicles (HGVs). HGV tires are designed on a tradeoff between wear resistance, rolling resistance, and adhesion (skid resistance). High wear resistance tires (high mileage) are replaced less often but use more fuel during operation, and vice versa for low rolling resistance tires. Presently, finding the optimal tire to minimize replacement costs and fuel consumption (greenhouse gas emissions) is challenging due to the difficulty in predicting tire wear for a given operation, since its rate varies with different vehicle configurations (e.g., load, vehicle length, number of axles, type of axle, etc.) and road types (e.g., motorways/highways, minor roads, urban roads, etc.). This article presents a novel empirical tire-wear model that can be used to predict the wear for multi-axle vehicles based on route data and a vehicle model. The first part of the article presents the analytical and experimental development of the model. The second part presents the experimental validation of the model based on 10 months of in-service data totaling 37,000 km of operation. The model predicts tire tread depth within 8% (average error of 2%).

Vehicle Length Estimation Using an LTE Transmitter Combined With a Software-Defined Receiver

Unobtrusive determination of an intruding vehicle's length is useful for threat assessments in perimeter surveillance, law enforcement, and antiterrorism applications. We describe a passive radar architecture wherein a broadcast signal from a cellular mobile communication system radio transmitter is bistatically scattered by a moving vehicle and received and processed in a software-defined radio platform. Measurement of the maximum Doppler frequency and the target viewing time combined with the known geometry of the measurement scenario permits the estimation of the target vehicle's length. Good agreement is observed between actual and estimated lengths for various vehicle types.