Road Type Research Articles

POTENSI AGROFORESTRI KOPI UNTUK WISATA ALAM DI KESATUAN PENGELOLAAN HUTAN LINDUNG BATUTEGI

POTENSI AGROFORESTRI KOPI UNTUK WISATA ALAM DI KESATUAN PENGELOLAAN HUTAN LINDUNG BATUTEGI

An AI Safety Monitoring System for Electric Scooters Based on the Number of Riders and Road Types.

Electric scooters are quickly becoming a popular form of mobility in many cities around the world, which has led to a surge in safety incidents. Moreover, electric scooters are not equipped with safety devices for riders, which can lead to serious accidents. In this study, a footrest, data-collection module, and accelerometer module for electric scooters were developed to prevent various accidents caused by the rapid increase in the use of electric scooters. In the experiment, the boarding data of the electric-scooter riders were collected from the footrest and data-collection module. Moreover, the driving data of the electric scooters for different road types were collected with the accelerometer module. We then trained two artificial intelligence (AI) models based on convolutional neural networks (CNNs) for different types of data. When we considered the learning accuracy and mean square error (MSE), which are performance indicators of the ability of the trained AI models to discriminate data, for each AI model, the learning accuracy converged to 100% and the MSE converged to 0. Further, this study is expected to help reduce the accident rate of electric scooters by resolving the causes of frequent accidents involving electric scooters around the world.

Adaptive Neuro-Fuzzy Control of Active Vehicle Suspension Based on H2 and H∞ Synthesis

This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.

Strategic Planning for Equitable RWIS Implementation: A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems (RWIS) station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables. Previous research primarily centered on single-variable analysis focusing on road surface temperature (RST). The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework. This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty. To demonstrate the effectiveness of this refinement, the authors applied the framework to Maine’s existing RWIS network, conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm. The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations. A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations. The location solutions generated have been adopted by MaineDOT for future implementations, attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

Do different types of urban streets lead to varying COVID-19 risk perceptions? An empirical study from a spatial heterogeneity perspective

People's risk perception varies across different environments. However, how different environments influence people's COVID-19 risk perception (RP), especially street environments, has yet to be fully evaluated. This study explores how three different types of street environments influence people's COVID-19 RP. First, we used pedestrian-centric street-view images and machine-learning models to predict the severity of COVID-19 RP in the study area. In addition, geographically weighted regression models were constructed to assess the locally most important environmental factors and their spatial heterogeneity. The results showed that openness was the most important influencing factor on COVID-19 RP in all three types of streets, while visual crowdedness was the least important one. Moreover, the high-value clusters of COVID-19 RP mostly occurred in poor- and medium-quality commercial streets, poor-quality residential streets, and leisure streets close to the plaza. Contrastingly, the low-value aggregations of COVID-19 RP mainly appeared in high-quality commercial streets, medium- and high-quality residential streets, and leisure streets close to blue space. We also found that the importance of environmental factors varied greatly by location. Interestingly, the RP was lower with higher visual crowdedness for all types of commercial streets. Based on this, the COVID-19 RP in different types of street environments was comprehensively evaluated so that city-relevant departments can take more effective measures to manage the public health risk based on geographical features.

Road Users Fail to Appreciate the Special Optical Properties of Retroreflective Materials.

To determine whether typical road users appreciate the special optical properties of retroreflective materials. Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials. Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration. In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance. The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night. Educational interventions could prove useful in this domain.

Towards sustainable transport: quantifying and mitigating pollutant emissions from heavy-duty diesel trucks in Northeast China.

Heavy-duty diesel trucks (HDDTs) have caused serious environmental pollution in China. Accurate estimation of their pollutant emission characteristics is essential to reduce emissions and associated environmental and public health impacts. To achieve sustainable development for transport emissions in Northeast China, we developed localized emission factors and a high-resolution emission inventory of HDDTs, based on on-board test, Guidebook and international vehicle emission (IVE) model. The results show that the total emissions of CO, NO, NO2, and PM from HDDTs in Northeast China in 2020 were 172.2 kt, 531.5 kt, 11.2 kt, and 921.4 t, respectively. In terms of spatial distribution, emissions decreased from the city center to the city fringe. Temporally, the NOx emission variation curves of different types of roads presented a "single-peak" emission characteristic, which was different from the peak of traffic flow. Three emission reduction scenarios are further developed in the paper. Scenario analysis shows that elimination of HDDTs that follow the old China III emission standard and installing tailpipe treatment devices are the most effective pollutant reduction measure. The reduction percentages for CO, NO, NO2, and PM ranged from 62.9 to 83.89%. The results of our study could inform policymakers to devise feasible strategies to reduce vehicle pollution in Northeast China.

An assessment of the safety and efficiency of log trucks with increased weight limits on interstate highways in Wisconsin and Minnesota, USA

ABSTRACT Design standards for the Federal Interstate Highway System in the US are generally higher than those on other roads within most states, making it the safest road system in the US. Federal law prevents states from enforcing vehicle weight limits on interstate highways that deviate from established Federal weight limits or state-specific grandfathered weight limits or exceptions. As a result, trucks hauling logs at state-legal limits must travel on other roads, passing through towns/cities and school zones where they may encounter on-coming traffic and intersections. All these encounters increase the risk of an accident. This study compared fatality rates of log trucks to other heavy trucks in the lower 48 states, road damage cost estimates for interstate and non-interstate roads and assessed the impact of relaxing interstate weight limits on various factors for hauling logs along three travel corridors in Wisconsin and Minnesota, USA. On a per-load basis, log trucks have a lower fatality rate than other heavy trucks in 83% of the lower 48 states. Due to the higher design standards, pavement damage costs are lowest on interstate highways as compared to other road types. Allowing state-legal, loaded log trucks access to federal interstate highways would improve the overall safety and efficiency of timber transportation and reduce pavement damage costs and CO2 emissions within the study areas. Overall, the study findings suggest that allowing state-legal, loaded log trucks to operate on interstate highways would improve the safety and efficiency of timber transportation in Wisconsin and Minnesota.

Accident Detection and Alert System Using Big Data Analytics

Road accidents are a serious hazard to life and limb and result in considerable financial damages on a global scale. For reducing reaction times and ensuring that victims receive aid quickly, quick and effective accident detection technologies are essential. A brief description of a crash monitoring and warning system that uses big data analytics to improve traffic safety is provided in this abstract. To correctly identify and anticipate accidents, the proposed system incorporates a variety of info sources such as real-time traffic data, meteorological information, and car telematics. The system can analyze huge amounts of disparate data in real-time by using modern data analytics techniques like machine neural networks and predictive modeling. Road accidents are becoming more commonplace across the world, which calls for the creation of cutting-edge technologies that can quickly identify incidents and notify the appropriate authorities for fast help. The use of big data analytics has developed in recent years as a viable strategy to improve accident identification and response. These systems are able to recognize possible accidents and produce alerts in real time by using enormous quantities of varied sources of data, such as real-time traffic data, meteorological conditions, and vehicle telematics. Data Preprocessing: To guarantee quality and consistency, collected data is preprocessed. This entails managing missing values, data normalization, noise reduction, and data cleaning. Relevant elements, such as traffic patterns, weather patterns, types of roads, and vehicle behavior, are retrieved from preprocessed data. Techniques for feature engineering turn unstructured data into useful representations. Alert generation: When an accident is detected, alerts are created and transmitted to the appropriate parties, such as medical professionals, law enforcement, and passing cars, with details on the accident's location, level of seriousness, and suggested next steps. System assessment: To determine the efficiency of the system in accurately detecting accidents and producing timely alerts, performance assessment is carried out using metrics including reliability, precision, recollection, and reaction time. Taken of Radar Model Example, Azimuth, Elevation, Horizontal resolution, Maximum detectable speed Taken of Evaluation parameters: Radar type, Short Range, Radar Mid-Range, Radar Long Range Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable Speed are alternative parameters. These parameters are defined in the materials and methods section. The figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and preference score. Every parameter is monitored to some extent in the graph, and the evaluation parameters are also precisely stated in the materials and techniques section. The Figure below represents an accident detection and alert system using big data analytics with performance value, weightage, weighted normal decision matrix, and the preference score the alternative parameters are Radar Model Example, Azimuth, Elevation, Horizontal Resolution, and Maximum Detectable speed which is defined as in the materials and methods section. The Evaluation parameters are also clearly defined in the materials and methods section, every parameter is measured to a certain degree in the graph.

Current Knowledge on the Transportation by Road of Cattle, including Unweaned Calves.

Transport conditions have the potential to alter the physiological responses of animals to the psychological or physical stress of transport. Transportation may introduce multiple physical and psychological stressors to unweaned calves and adult cattle, including noise, overcrowding, food and water deprivation, extreme temperatures, commingling with unfamiliar animals, handling by unfamiliar humans, and being placed in a novel environment upon arrival. Apart from these factors, the type of road and even driving skill may affect the welfare of animals. One of the concerns regarding cattle transport is that the handling and marketing of animals prior to a journey may lengthen the period of feed withdrawal. Furthermore, feed withdrawal can impact animal welfare through hunger and metabolic stress. Transportation is also associated with a decrease in animal performance as well as an increase in the incidence of bovine respiratory disease. It is well established that the transportation of cattle is a stressor that causes a quantifiable response; however, excessive stress during transport resulting in physiological or pathological changes can be reduced with best management practices. The objective of this review was to analyse the available scientific literature pertaining to the transport by road of cattle, including unweaned calves.

Level of road user satisfaction with safety and confort on jalan Mohammad hatta

Mohammad Hatta Road is a type of district road that is used as a link between Kurik District and MalindiMalindiMalindi District. The increased activity and mobility of residents on Jalan Mohammad Hatta have not been balanced with safety and comfort for road users, such as damage to the road body and a lack of supporting facilities on the road. The purpose of this study was to determine the factors that influence the lack of safety and comfort levels of road users and the influence between satisfaction variables on safety and comfort variables on Jalan Mohammad Hatta. This research method uses the correlation test method. This test is used to find the relationship between variable X (satisfaction) and variable Y (safety and comfort) on Jalan Mohammad Hatta which has bad road conditions. The data used in this study were obtained by distributing questionnaires with a total sample of 394 respondents. The results of this study showed that people were dissatisfied a percentage of 83.61%, insecure with a percentage of 83.92%, and very uncomfortable with percentage of 84.01%, in terms of several factors, namely road conditions, stability of vehicles, health, travel effectiveness, economy, pavement structure, socio-cultural conditions, facilities, and accessibility. There is a relationship between the satisfaction variable and the safety variable with a correlation value of 0.905 and a relationship between the satisfaction variable and the comfort variable with a correlation value of 0.886. So it shows that the higher the level of safety and comfort, the higher the level of satisfaction of road users.

Experimental investigation on performance of composite thermochromic and ice-melting coatings for dynamic pavement temperature control

As the main type of urban roads, the surface temperature of asphalt pavement can reach 70 °C in summer because high heat absorption, causing pavement disease and aggravating the heat island effect. Solar reflective coating is an effective way to tackle the problem in summer but may bring certain negative effects in winter. In this paper, a kind of composite coating containing thermochromic (TCM) materials and snow melting and ice suppression (SMIS) functional fillers was developed. The reflective properties, thermal behavior and road performance of the pavement coatings were investigated. The results showed that the optimal TiO2 contents in TCM-black coating for pavement was 3 wt% to make full use of optical variation range. Long-term solar radiation destroyed the structure of thermochromic microcapsules and caused degradation of color and visible reflectance of TCM coatings, which could be alleviated by ultraviolet absorbers and light stabilizers. The optimal double-layer coating structure with TCM on the top and SMIS at the bottom could achieve cooling effect of 9 °C, pavement icing temperature of −5 °C and faster ice melting rate. The skid and temperature change resistance with the coatings also met requirements. This study provides a reference for developing novel pavement materials with balanced cooling effect and ice suppression capacity for mitigating heat island effect, improving driving safety, and ensuring the optimal service state and longer service life of pavement at the same time.

Indicator Construction of Road Surface Deformation Activity in Cold Regions and Its Relationship with the Distribution and Development of Longitudinal Cracks

As a type of road distress in cold areas, longitudinal cracks have a high incidence and cause serious damage. The occurrence of longitudinal cracks is related to the conditions of the subgrade, pavement structure, material properties, and water temperature. The goal is to gain a deeper understanding of the occurrence mechanism of longitudinal cracks and provide references for the prevention and control of longitudinal cracks. Through the monitoring of vertical deformation, longitudinal crack distribution, and the development of typical roads in cold areas, the discrete characteristics and the variance in the distribution of deformations were analyzed. The construction of an activity index based on the variance of time-series elevation and the standard deviation of elevation change was used to describe the activity level of road sections, longitudinal lines, intervals, and longitudinal deformation. Based on the correlation between vertical deformation and longitudinal cracks on the road surface, the relationships among the activity, condition, distribution, and development characteristics of longitudinal cracks were analyzed. The results indicate that there were significant differences in the deformation activity of the road surface at different times and that the activity was greater during the freezing and thawing periods. The development and distribution characteristics of longitudinal cracks were significantly correlated with activity level. This study can help improve our understanding of the dynamic deformation characteristics of road surfaces under natural conditions and the relationship between transverse distribution differences and longitudinal cracks. It can also provide clarifications and references for the development of the roadbed, pavement structure and materials, the mechanics of the pavement structure, the emergence of distresses, and the laws of development in cold areas.

The pattern of maxillofacial fractures associated with rollover accidents: A 7-year retrospective study.

This study aimed to assess the incidence and pattern of maxillofacial fractures and related demographic data in the victims of rollover crashes. This retrospective study was based on medical records of patients who sustained maxillofacial injuries following rollover accidents. Investigated data included age, gender, accident date and time, accident cause, seat belt usage, airbag deployment, road type, anatomical location of the facial fracture, and treatment approach. Among the 147 patients who met the inclusion criteria, the most prevalent age groups were 20-30 (36.7%) and 30-40 (32.7%) years, with a mean age of 33.6 ± 9.7 years. The male-to-female ratio was 6:1. Most crashes occurred in March, August, and July. These accidents involved 69.4% light vehicles and 39.6% heavy vehicles. The leading causes of rollover crashes were speeding (58.5%) followed by distracted driving (21.1%) and traffic rule violations (13.6%). The most prevalent injuries were fractures of the maxillary sinus wall (40.8%), nasal bones (39.5%), zygomaticomaxillary complex (36.1%), and the mandible (32.6%). Surgical intervention was necessary for 44.2% of patients, while 12.9% of cases underwent close reduction, and 42.9% did not require any surgical intervention. The occurrence of nasal bone fractures was significantly lower in cases where seat belts were worn and zygomatic arch fractures were less frequent in incidents with airbag deployment. In rollover crashes, the midface is the most vulnerable anatomical location. Utilization of seat belts and airbag deployment has the potential to prevent nasal bone and zygomatic arch fractures.

Coupling relationships between urban block spatial morphology and microclimate in severe cold regions

This study investigates the relationship between urban block spatial morphology and microclimate in severe cold regions, using Shenyang, China as a case study. We employed computational fluid dynamics theory-based numerical simulation software and a controlled variable approach to analyze the microclimate effects of four key aspects: street conforming line ratio, street interface density, street aspect ratio, and building roof forms. The primary findings are as follows: Decreasing conforming line ratios initially increase average wind speed and temperature. Lower interface densities reduce average wind speed but raise temperature. Higher aspect ratios correspond to increased wind speed and decreased temperature. Additionally, upward sloping roofs correlate with higher average wind speed and temperature. This research provides a perspective for evaluating urban microclimates, considering human perception of urban block space. It also suggests spatial layout design strategies for different types of streets in severe cold regions, considering the climate environment.

Influence of seasonal activities and traffic conditions on the accumulation and particle size distribution of gully pot sediments

Gully pots (GPs) are ubiquitously used in urban catchments, to direct surface runoff into piped sewer systems, reduce the risk of sediment-induced pipe blockages and the loading of solids and associated contaminants on the receiving waters. Over time, the build-up of sediments in GPs will lead to impaired hydraulic performance, putting the urban catchment at risk of flooding. However, GP maintenance strategies lack both a robust evidence base and clear aims against which their effectiveness can be benchmarked. An improved understanding of the factors influencing long-term in-situ solids accumulation in GPs is required for further assessment and optimisation of the GP maintenance scheme. As a contribution to addressing these knowledge gaps, sediments from 27 GPs located in urban catchments associated with three street feature types (roundabout; crossing; straight road) and varied traffic intensities were collected on two occasions, representing two distinct seasonal accumulation periods. Parameters including GP fullness levels, particle size distribution (PSD), and solids mass accumulation rates were evaluated with regards to the impacts of seasonal activities, traffic conditions as well as configuration and dimension of GPs. The results indicate impacts of seasonal activities on solids accumulation rates were not consistent across all GPs, with the magnitude of the difference larger following winter-spring catchment practices. Additionally, synergistic impacts of seasonal activities and traffic conditions on PSD and solids accumulation rates were observed. For example, the winter road safety measures may enhance the impacts of the characteristic vehicle turning motions at roundabouts, contributing to the negative correlation identified between traffic intensities and solids accumulation rates. The overall exhibited large variations in sump fullness levels (4 %–73 %) and solids dry mass (0.5–51 kg) directly challenged the commonly adopted generalised GP maintenance frequency without taking the catchment-specific activities into consideration.

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.

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.

Direct measurement of brake and tire wear particles based on real-world driving conditions

With implementing vehicle emission control policies, tailpipe particulate emissions have been gradually controlled, and the relative contribution of non-tailpipe particulate emissions, such as brake and tire wear, has further increased. A unified and scientific method for sampling non-tailpipe particulate matter (PM) emissions is essential to improve the accuracy of the emission characteristics and factors. This study proposes a novel sampling method based on real-world driving conditions to obtain information on emissions and extract characteristic conditions for tire and brake pad wear. We extracted 200 representative braking segments for simulation experiments based on road type, initial and final velocities, temperature, and deceleration rate. Two standard test cycles to simulate the tire wear conditions of the front and rear wheels were constructed based on velocity, lateral, and vertical forces. Under the real-world driving condition test cycle, the emission factors of PM2.5 and PM10 for brake wear particles of passenger vehicles were 2.66 mg/km and 11.65 mg/km, respectively. In contrast, the emission factors of PM2.5 and PM10 for tire wear particles were 0.21 mg/km and 1.27 mg/km, respectively. Moreover, this study provides insights and basic data for localizing and improving the emission model, which can enhance its applicability and accuracy.

SoC-VRP: A Deep-Reinforcement-Learning-Based Vehicle Route Planning Mechanism for Service-Oriented Cooperative ITS

With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the Vehicle Routing Problem (VRP) is a significant concern addressed in this work. Considering the varying urgency levels of different vehicles and their different traveling constraints in the Service-oriented Cooperative ITS (SoC-ITS) framework studied in our previous research, the Service-oriented Cooperative Vehicle Routing Problem (SoC-VRP) is firstly analyzed, in which cooperative planning and vehicle urgency degrees are two vital factors. After examining the characteristics of both VRP and SoC-VRP, a Deep Reinforcement Learning (DRL)-based prioritized route planning mechanism is proposed. Specifically, we establish a deep reinforcement learning model with Rainbow DQN and devise a prioritized successive decision-making route planning method for SoC-ITS, where vehicle urgency degrees are mapped to three priorities: High for emergency vehicles, Medium for shuttle buses, and Low for the rest. All proposed models and methods are implemented, trained using various scenarios on typical road networks, and verified with SUMO-based scenes. Experimental results demonstrate the effectiveness of this hybrid prioritized route planning mechanism.