Road Segments Research Articles

A tensorial weighted Schatten-[formula omitted] norm model with neighbor regularization for traffic data completion and traffic system correlation exploration

Traffic data exhibit the spatiotemporal heterogeneity. Revealing traffic system correlations (TSC) from traffic data, which refers to discovering inter-station correlations incurred from movements of passengers, is a meaningful task for releasing traffic congestion and sustainable urban operations. Since traffic data involve a great loss or corruption due to equipment and transmission failures, traffic data completion (TDC) serves as a necessary task for TSC exploration. We observe that: for the TDC task, traffic data exhibit anisotropic periodicity and repetition along different temporal dimensions, which cannot be captured by traditional low-rank TDC methods; for the TSC exploration task, a traffic network obeys graphical structure and exhibits a strong spatial correlation between stations or road segments. Based on these observations, we propose a Tensorial Weighted Schatten-pNorm model with Neighbor Regularization (TWSN2R) for accomplishing the two tasks simultaneously. TWSN2R consists of a tensorial weighted Schatten-p norm term and a neighbor regularization term. The first term mines the anisotropic periodicity with a tensor form of weighted Schatten-p norm which preserves suitable rank components of traffic data by allocating different weights in both tensorial dimension and decomposed mode dimension. The second term mines a spatial correlation of a traffic network by enforcing a neighbor contribution of passenger flows incurred from the network, and extrinsically reveals an inter-station (or inter-road-segment) TSC of the network. Experiments on the subway flow dataset MetroBJ and the road network vehicle speed dataset RoadBJ demonstrate that, TWSN2R not only outperforms several state-of-the-art TDC methods, but also reveals two pieces of regulations of TSC. (a) For subway passenger flows, positive correlated stations and negative correlated stations contribute to passenger flows differently according to each station’s attributes. (b) For road networks, urban loops, expressways and transportation hubs contribute to the vehicle flows at a majority, and serve as the critical roads for mitigating the congestion of whole road networks.

Shortest Path Problems with a Crash Risk Objective

With more and more data related to driving, traffic, and road conditions becoming available, there has been renewed interest in predictive modeling of traffic incident risk and corresponding risk factors. New machine learning approaches in particular have recently been proposed, with the goal of forecasting the occurrence of either actual incidents or their surrogates, or estimating driving risk over specific time intervals, road segments, or both. At the same time, as evidenced by our review, prescriptive modeling literature (e.g., routing or truck scheduling) has yet to capitalize on these advancements. Indeed, research into risk-aware modeling for driving is almost entirely focused on hazardous materials transportation (with a very distinct risk profile) and frequently assumes a fixed incident risk per mile driven. We propose a framework for developing data-driven prescriptive optimization models with risk criteria for traditional trucking applications. This approach is combined with a recently developed machine learning model to predict driving risk over a medium-term time horizon (the next 20 min to an hour of driving), resulting in a biobjective shortest path problem. We further propose a solution approach based on the k-shortest path algorithm and illustrate how this can be employed.

Uncovering the association between traffic crashes and street-level built-environment features using street view images

Investigating the relationship between built environment factors and roadway safety is crucial for preventing road traffic accidents. Although studies have analyzed traffic-related built environment factors based on pre-determined zonal units, conclusive evidence regarding the relationship between streetscape features and traffic accidents at a fine-grained road segment level is still lacking. With the widespread availability of large-scale street view images, automatically analyzing urban built environments on a large scale is possible. Therefore, the aim of this study was to investigate the relationship between streetscape features and traffic accidents at a fine-grained road segment level using street view images. Specifically, we employed semantic image segmentation to extract streetscape elements from urban street view images, and then created traffic crash-related variables, including the street-level built environment variables, traffic variables, land-use indices, and proximity characteristics, at the road-segment level. Finally, we adopted a classification-then-regression strategy to model the number of traffic crashes while considering the zero-inflated and spatial heterogeneity issues. Our findings suggest that streetscape features can effectively reflect built-environment characteristics at the road-segment level. Moreover, a comparison of our proposed modeling method with existing models demonstrates its superior performance. The results provide insight into the development of effective planning strategies to improve traffic safety.

Multi-task-based spatiotemporal generative inference network: A novel framework for predicting the highway traffic speed

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time, and it also provides valuable reference data for traffic control in advance. Three essential elements should be considered in highway traffic speed prediction: (1) dynamic spatiotemporal correlation, (2) prediction error propagation elimination, and (3) traffic speed heterogeneity on the highway network. A multi-task-based spatiotemporal generative inference network (MT-STGIN) is proposed to address the above challenges. 11https://doi.org/10.24433/CO.8500259.v1. MT-STGIN consists of three parts: an encoder based on spatiotemporal correlation extraction block (ST-Block), a decoder based on masked ST-Block and generative inference, and multi-task learning. The encoder is first used to extract the dynamic spatiotemporal correlation of the highway network. The decoder concentrates on correlating historical and - target sequences and generates the target hidden outputs rather than a dynamic step-by-step decoding way. Finally, a multi-task learning method is used to predict traffic speed on different types of road segments because of heterogeneity and shares the underlying network parameters. The evaluation experiments use the monitoring data of the highway in Yinchuan City, Ningxia Province, China. The experimental results demonstrate that the performance of the proposed prediction model is better than that of the baselines, and it can efficiently solve the problem of long-term highway speed prediction.

Modelling concentration heterogeneities in streets using the street-network model MUNICH

Abstract. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street.

A Multiscale Assessment of the Impact of Perceived Safety from Street View Imagery on Street Crime

Perceived safety of the built environment—a cognitive assessment different from emotional fear of crime—might affect the number of potential crime victims in an area and thus affect crime opportunities. The perceived safety derived from street view imagery has propelled scholars to examine its relationship with crime. The literature, however, has not addressed the related geographic scale variability issue; that is, the choice of the geographic analytical units might affect the relationship between area-based perceived safety and crime. This study explores how the relationships between street-view-derived perceived safety and both street thefts and street robberies vary by different spatial scales in Cincinnati. Results of negative binomial models show that perceived safety is positively associated with street thefts and street robberies at both the street segment and census block levels, but is negatively associated with these crimes at the census block group level. The relationship is not statistically significant at the census tract level. This variability is explained by the different freedom of avoidance behaviors in response to perceived safety, which change by geographic scale. The research further evaluates the within variance and between variance of perceived safety at different scales. Compared to between variance, within variance is smaller at both the street segment and block levels, but larger at both the block group and tract levels. This variability can be a source of model instability across multiple geographical scales. In short, the multiscale assessment shows that larger spatial units like the census tract are unsuitable for perceived safety–crime analysis.

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

The significance of critical infrastructure systems in maintaining productivity is undeniable. However, such systems remain susceptible to external disturbances and cascading failures. Instead of operating independently, these physical systems, such as transportation and stormwater systems, form an interdependent system. This interdependence, particularly important during flooding, illustrates that the failure of a stormwater system can disrupt traffic networks. To explore the extent of such interdependency, this study investigates the transportation and stormwater networks in Norman, Oklahoma. Using network science theories and concepts of multilayered networks, this paper analyzes these systems, both individually and in combination. The study identifies closely located components in the road and stormwater networks using Moran's I spatial autocorrelation metric. Next, the connectivity of these networks is represented in a graph format to investigate the topological credentials (i.e., rank of relative importance) of the network components (i.e., water inlets, road intersections as nodes, and stormwater conduits, road segments as links). Moreover, such credentials further change by considering the weights of the network components (i.e., average daily traffic, water flow). The proximity-based connectivity considerations between these networks utilizing Moran's I significance score revealed a good indicator of spatial interdependency. When incorporating directionality, the multilayer network analysis highlights that highly central components tend to cluster spatially, unlike the undirected counterpart. The study also identifies vulnerable locations and network components in a combined network setting that differ from the networks in isolation. In doing so, the research reveals new insights governing the complex reliance of transportation systems on neighboring stormwater systems.

Bridging the gap between pedestrian and street views for human-centric environment measurement: A GIS-based 3D virtual environment

Street View Image (SVI) data has gained popularity as a methodological tool for assessing pedestrian-level environments across various disciplines. Several studies in transportation and public health fields posit that environmental features measured through SVIs are similar to those perceived by pedestrians, whereas, in fact, SVIs are typically collected from vehicles. As a result, the SVI data may reflect significant differences from an actual pedestrian’s view. Hence, this study investigates the errors in measuring neighborhood environmental features resulting from the difference between pedestrian and vehicle views. To accomplish this goal, a three-dimensional (3D) urban model is developed, utilizing multiple Geographic Information Systems (GIS) data. This model accurately represents the spatial positioning and dimensions of various entities. From the 3D model, virtual SVIs for each perspective are collected. Subsequently, we quantify environmental features in a manner similar to semantic segmentation for the images. Our findings reveal significant measurement errors for sidewalks, greenery, and roads between pedestrian and vehicle views. These errors are more pronounced with certain environment characteristics and parameter settings for image acquisition. For example, narrow road segments with few on-street parked vehicles exhibit increased measurement errors for greenery. Overall, the study provides guidelines for researchers on utilizing SVI to achieve reliable measurement of environmental features and move toward human-centric measurements.

Road traffic flow prediction based on dynamic spatiotemporal graph attention network

To improve the prediction accuracy of traffic flow under the influence of nearby time traffic flow disturbance, a dynamic spatiotemporal graph attention network traffic flow prediction model based on the attention mechanism was proposed. Considering the macroscopic periodic characteristics of traffic flow, the spatiotemporal features are extracted by constructing spatiotemporal blocks with an adjacent period, daily period, and weekly period respectively. The spatiotemporal block is mainly composed of a two-layer graph attention network and a gated recurrent unit to capture the hidden features of space and time. In space, based on considering adjacent road segments, the Pearson correlation coefficient is used to capture the hidden correlation characteristics between non-adjacent road segments according to a certain time step. In terms of time, due to the random disturbance of traffic flow at the micro level, the attention mechanism is introduced to use the adjacent time as the query matrix to weight the output characteristics of daily cycle and weekly cycle, and the three are connected in series to output the prediction results through the linear layer. Finally, the experimental results on the public data sets show that the proposed model is superior to the six baseline models.

A Framework for Determining Collision Likelihood Using Continuous Friction Values in a Connected Vehicle Environment

Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warn drivers beforehand about hazardous roads. In practice, however, discerning between safe and unsafe roads is sometimes unclear due to intermediate RSC classes covering too wide a range of conditions. This study aims at solving this safety ambiguity issue by proposing a framework for predicting collision likelihood within a road segment. The proposed framework converts road surface images into friction coefficients, which are then converted into continuous measurements through an interpolator. To find the best-performing interpolator, we evaluated geostatistical, machine learning, and hybrid interpolators. It was found that ordinary kriging had the lowest estimation error and was the least sensitive to changes in distance between measurements. After developing an interpolator, collision likelihood models were developed for segment lengths ranging from 0.5 km to 20 km. We chose the 6.5 km model based on its accuracy and intuitiveness. This model had 76.9% accuracy and included friction and AADT as predictors. It was also estimated that if the proposed framework were implemented in an environment with connected vehicles and intelligent transportation systems, it would offer significant safety improvements.

Detecting Turning Relationships and Time Restrictions of OSM Road Intersections from Crowdsourced Trajectories

OpenStreetMap (OSM) road networks provide public digital maps underlying many spatial applications such as routing engines and navigation services. However, turning relationships and time restrictions at OSM intersections are lacking in these maps, posing a threat to the accuracy and reliability of the services. In this paper, a new turn information detection method for OSM intersections using the dynamic connection information from crowdsourced trajectory data is proposed to address this problem. In this solution, the OSM intersection structure is extracted and simplified and crowdsourced trajectories are projected onto OSM road segments using an improved Hidden Markov Model (HMM) map matching method that explicitly traces the turning connections in road networks. Optimal path analysis increases the turning support related to short road segments. On this basis, this study transforms complex turning identification scenarios into the simple analyses of traffic connectivity. Furthermore, a voting strategy is used to identify and calculate turning time restrictions. The experimental results, using trajectory data from three cities in China, show that the turning relationships can be detected at a precision of 90.71% with a recall of 96.55% and an F1-value of 93.54% in Shanghai. For Wuhan, the precision is 95.33% and the recall is 95.00%, with an F1-value of 95.16%. The precision and recall when identifying turning time restrictions both reach 90% in Xiamen. These results demonstrate the effectiveness of the proposed turning detection method.

Route optimization of hazardous freight transportation in a rail-truck transportation network considering road traffic restriction

With the increasing demand for chemical products in China, how to manage the risks associated with hazardous freight transportation has attracted arising attentions from both government and industry practitioners. From the perspective of carrier companies, transportation modal choice and routing for hazardous freight transportation need to be carefully designed, which motivates current research. This paper aims to optimize the service mode and routing for hazardous freight transportation in a rail-truck network with bi-objectives optimization approach. Several practical concerns are addressed, including the coordination of rail transportation which has fixed train running plan and road transportation which is flexible and mobile, and the consideration of alternative path when the expressway is temporally blocked for hazardous freight due to the traffic restriction measurements on inter-city road segments. Furthermore, the time-varying transportation risks and costs associated with accidents of hazardous freight transportation are investigated. The Max-Min Ant-colony Algorithm (MMAS) algorithm is adopted for solution. For model validations and conclusion generalization, this paper implements a real-life case study in Beijing-Tianjin-Hebei region in China with the objective of minimizing transportation risks and costs. The solutions show that traffic restrictions have an adverse impact on the carrier and alternative paths are able to help the carriers further optimize their routing strategy. A careful consideration and appropriate processing of differences in operational characteristics of two transportation modes, is able to provide a better and more realistic understanding of rail-truck hazardous freight transportation problem.

PENGARUH HAMBATAN SAMPING TERHADAP KINERJA JALAN (STUDI KASUS: JALAN PELITA DAN JALAN FLAMBOYAN, JALAN SEKITAR TAMAN SANGKAREANG, KOTA MATARAM)

Some traffic problems often arise due to an activity from the side of the road segment. Congestion is a problem that often occurs in urban road traffic. Congestion is caused by a lack of ability or capacity of the road to provide services to the volume of vehicles crossing a certain road section as well as the presence of high side friction conflicts that cause vehicle speeds to slow down which results in a non-smooth traffic flow.Sangkareang Park is one of the areas located in the center of Mataram City which is a public facility for the community. This resulted in an increase in side barriers on roads around Sangkareang Park. This study aims to determine the value of side friction and road performance on roads around Sangkareang Park, namely Jalan Flamboyan and Jalan Pelita. Furthermore, this research was conducted to determine the effect of the side friction on road performance as described by speed and degree of saturation.The results of the study on the Flamboyan road section had the highest side resistance, speed, and degree of saturation values of 814.8 incidents/hour, 32.53 km/hour, and 0.09. On the Pelita road, the highest values of side resistance, speed, and degree of saturation were 1614.6 incidents/hour, 30.90 km/hour, and 0.16 respectively. The results of the simultaneous regression analysis found that the side resistance affects the speed and degree of saturation. The results of the partial regression analysis show that on the Flamboyan road, the side friction variable that has the most influence on speed and degree of saturation is Slow Vehicles (SMV) while on Pelita road, the side resistance variable that has the most effect on speed and degree of saturation is vehicles entering and leaving (EEV). ) followed by the parking and stopping vehicle variable (PSV).

Study on Road Weather Recognition Method Based on Road Segmentation

Study on Road Weather Recognition Method Based on Road Segmentation

Unprotected Left-Turn Behavior Model Capturing Path Variations at Intersections

Path dispersion (the spatial distribution of vehicular paths) is an important feature of traffic flow inside intersections and differs from traffic flow running along traffic lanes at road segment, especially under conflicting movements. The path dispersion reflects the operational features of traffic flow and is related to driving behaviour, arrival flow patterns, layout design, and the traffic control and management scheme. This study aims to improve the understanding of the overall path dispersion of unprotected left-turns and the opposing through movement. A behavioural simulation model was established to represent the overall path dispersion. Human behaviours regarding vehicle trajectory planning with and without conflicting vehicles were modelled based on optimal control and integrated into the proposed discrete event simulation framework. The descriptive power and accuracy of the proposed simulation model were validated using empirical data. The effects of the spatial size of the intersection, crossing angle, and traffic volume on the path dispersion of the left-turn and through movement were explored based on numerical experiments. The results show that the proposed simulation model can represent the path dispersion of left-turn and opposing through movement well for both the calibrated intersections and newly added intersections without model parameter recalibration with an average error of 8.92%.

Semantic road segmentation based on adapted Poly-YOLO

With artificial intelligence continuing to change people’s everyday life in profound ways, the desire to endow vehicles with the ability to drive autonomously has emerged for years. Thus, autonomous driving has become a popular field. The autonomous driving task can be divided into three general procedures: perception, planning, and locomotion. The first and foremost part of these general procedures is the perception task. Among those perception methods, the most prevailing one is semantic segmentation, which is annotating and predicting the object located at the pixel level, meaning nearly all pixels should be classified into certain categories. However, this method provides enough accuracy while bringing a considerable computational burden. Thus, implementing real-time road semantic segmentation on autonomous driving vehicles is still a costly task. In this paper, an adapted model improved upon the Poly-YOLO baseline model is proposed, which is a well-developed object detection algorithm providing bounding polygons to enclose the target object, forming a polygon mask similar to that of semantic segmentation. This paper endeavors to enhance the model’s accuracy in detecting variously sized targets greatly and to fine-tune the model to generate more proximate enclosing polygons. The adapted model has experienced a leap in performance compared to the baseline Poly-YOLO model.

An Adaptive ECMS Based on Traffic Information for Plug-in Hybrid Electric Buses

Nowadays, a growing interest is in improving the efficiency of a parallel hybrid electric bus (PHEB). This paper proposes an adaptive energy management strategy based on an equivalent consumption minimization strategy (ECMS) by using real-time traffic information described by the average speed, average acceleration, and standard deviation of speed for different road sections. The whole adaptive energy management system consists of offline and online components. Three velocity characteristic parameters are derived by utilizing the data from the vehicle. Firstly, the road segments are classified through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means. Then three Markov transition matrices on different types of roads are constructed with the velocity feature parameters which can be used to predict the speed in the following time. On the other hand, the online component implements the energy management strategy (EMS) involved. The EMS incorporates predicted vehicle speed and road information into the ECMS. Based on this, the predicted vehicle speed and traffic information are applied to make adaptive changes to the equivalent factors in the ECMS. Finally, the performance of the proposed strategy is evaluated through a comparative simulation study and the results show the superiority of the approach in improving fuel economy. Comparative outcomes from HIL tests additionally illustrate that the proposed strategy's fuel efficiency is compatible with the original design intent.

Zone-Enhanced Spatio-Temporal Representation Learning for Urban POI Recommendation

Points-of-interest (POIs) recommendation plays a vital role in location-based social networks (LBSNs) by introducing unexplored POIs to consumers and has drawn extensive attention from academia and industry. Existing POI recommender systems usually learn fixed latent vectors to represent both consumers and POIs from historical check-ins and make recommendations under the spatio-temporal constraints. However, we argue that the existing works still suffer from the challenges of explaining consumers' complicated check-in actions. To this end, we first explore the interpretability of recommendations from the POI aspect, i.e., for a specific POI, its function usually changes over time, so representing a POI with a single fixed latent vector is not sufficient to describe the dynamic nature of POIs. Besides, check-in actions to a POI are also affected by the zone where it is located. In other words, the zone's embedding learned from POI distributions, road segments, and historical check-ins could be jointly utilized to enhance POI embeddings. Along this line, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ime-z <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> ne-space <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> OI embedding model ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ToP</b> ), which integrates multi-knowledge graphs and topic model to introduce not only spatio-temporal effects but also sentiment constraints into POI embeddings for strengthening interpretability of recommendation. Specifically, ToP learns multiple latent vectors for a POI in a different period with spatial constraints via knowledge graph learning. To add sentiment constraints, ToP jointly combines these vectors with the zone's representations learned by topic models to make explainable recommendations. ToP considers the time, space, and sentiment of POI in a unified embedding framework, which benefits the POI recommendations. Extensive experiments on real-world Changchun city datasets demonstrate that ToP achieves state-of-the-art performance in terms of common metrics and provides more insights for consumers' POI check-in actions.

DMGAN: Dynamic Multi-Hop Graph Attention Network for Traffic Forecasting

In the intelligent transportation system, traffic forecasting, which is generally characterized as a graph spatial-temporal prediction task, plays a crucial role. It is challenging to generate reliable forecast results due to the complexity of traffic topological information and the inherent uncertainty of road traffic circumstances. Existing works generally focus on modeling spatial dependency on static graph structures, but ignore dynamic relations between road segments and cannot extract long-range traffic dependencies in spatial-temporal domains. To bridge the above gaps, we present a novel framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Multi-Hop Graph Attention Network</i> (DMGAN). Specifically, we leverage dynamic graph modeling to capture time-varying relations across road sections and introduce the multi-hop operation in each message propagation layer to extract long-range spatial dependency. Meanwhile, we develop a fusion-attention module, preserving both local and global hidden layer outputs of the encoder, to capture both long- and short-term temporal dependencies jointly. In this way, our method can fully model complex time-varying traffic topology information and capture the internal patterns of traffic series by integrating dynamic graph structure and temporal attention component. DGMAN achieves state-of-the-art performance in three metrics, as demonstrated by experimental findings on four real-world public traffic datasets, METR-LA, PEMS-BAY, PEMS03, and PEMS07. This code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/EEHITer/2022-TKDE-DMGAN-Pytorch/tree/main</uri> for reproducibility and further studies.

Wildlife roadkill in Southwestern Ethiopia: Hotspots, drivers, and victim species

Wildlife-vehicle collision (WVC) is one of the major causes of wildlife mortality and a concern for conservationists worldwide. The study of roadkill data that can be used to develop appropriate measures and strategies for both wildlife conservation and traffic safety to mitigate the WVC and animals' mortality. Extensive studies have been conducted in the developed countries on the identification of WVC hotspots and its potential impact; however, less attention is given in developing countries. Because of this, the problem is not well understood in developing countries. This study was intended to identify victim species, hotspot areas for roadkill, and factors that contribute to WVC in Jimma Zone, along three roads leading from Jimma City. Data were collected using a citizen science approach and a skilled man power road survey (March–September 2022) to document the number and type of species killed. Three road segments (135 km in total) were surveyed weekly as a baseline for future research. All statistical analyses were carried out using R-software. In the present study, a total of 84 roadkill specimens were collected from 16 different mammal species, of which 85.7% were medium-sized carnivores. The findings showed that Jackals (Canis mesomalis and Canis adustus) were the most frequently killed species (25%, n is 21), followed by Civettictis civetta (22.62%, n is 19). The study revealed that the major factors causing collisions include habitat type (forest cover) and wildlife behavior (nocturnal or diurnal). Further, the study revealed that the majority of WVC occurred at night (dusk) and at dawn in areas that are covered with dense forests. To minimize the potential risks of WVC and animals’ mortality, we suggest putting warning signs in hotspot areas where roadkill usually occur frequently. In addition, we recommend creating awareness among drivers, traffic police, and all communities about the negative impacts and consequences of collisions on the ecological and economic value of wildlife.