Road Topology Research Articles

Layout Selection for an Optimal Sewer Network Design Based on Land Topography, Streets Network Topology, and Inflows

This paper proposes a methodology for the layout selection of an urban drainage system as an extension to the methodology for an optimal sewer network design proposed by Duque, Duque, Aguilar, & Saldarriaga. The layout selection approach proposed in this paper uses an objective function that takes into account all input data in the problem, such as: land topography, street network topology, and inflow to each manhole. Once the layout is selected, the network is optimally designed using dynamic programming. The problem of layout selection is solved as a mixed-integer programming problem and is divided into two steps. The first step tries to define an initial layout using the network topology and land topography as a criterion. This allows for an initial hydraulic design and an approximation of the sewer network’s construction costs. The second step uses the data obtained in the previous process to establish an approximation of the construction costs of each arc that can be part of the layout. This is in order to minimize the objective function of the layout selection problem so that the hydraulic design cost is also minimized. The methodology was successfully tested on three case studies: the Chicó sewer network proposed by Duque et al. and two sewer network benchmarks from the literature.

Fastening the initial access in 5G NR sidelink for 6G V2X networks

The ever-increasing demand for intelligent, automated, and connected mobility solutions pushes for the development of innovative vehicular communications technology in the framework of the sixth Generation (6G) of cellular networks. A radical transformation on the physical layer is planned, with a paradigm shift towards beam-based millimeter Waves or sub-Terahertz communications, which require precise beam pointing for guaranteeing the communication link, especially in high mobility. A key design aspect is a fast and proactive Initial Access (IA) algorithm to select the optimal beam to be used. In this work, we investigate alternative IA techniques to fasten the current fifth-generation (5G) standard approach, targeting an efficient 6G design. First, we discuss cooperative position-based schemes that rely on the position information. Then, motivated by the intuition of a non-uniform distribution of the communication directions due to road topology constraints, we design two Probabilistic Codebook (PCB) techniques of prioritized beams. In the first one, the PCBs are built leveraging historical traffic information, while in the second one, we use the Hough Transform over the digital map to extract dominant road directions. We also show that the information coming from the angular probability distribution allows designing non-uniform codebook quantization, reducing the degradation of the performances compared to uniform one. Numerical simulation on realistic scenarios shows that PCB-based beam selection outperforms the 5G standard in terms of the number of IA trials, with a performance comparable to position-based methods, without requiring the signaling of sensitive information.

Assessment of transportation system disruption and accessibility to critical amenities during flooding: Iowa case study

Natural disasters, such as flooding, can cause severe social, environmental, and economic damage to a community. Transportation infrastructure plays an essential role in flood response and recovery efforts. However, flooding may disturb road functionality and generate direct and indirect adverse impacts, including the loss of access to essential services. This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses. We assessed the disruption of transportation networks on the accessibility to critical amenities (e.g., hospitals) under 100 and 500-year flood scenarios. Our analysis methodology leads toward the development of an integrated real-time decision support system that will allow decision-makers to explore “what if” flood scenarios to identify vulnerable areas and population in their authority. These analyses could promote possible improvements (e.g., temporary relocation of critical services) to mitigate the consequences of road system failure during flooding. Due to varying environmental conditions at specific locations and effects on road topology under flood events, the results show differential impacts in edge and node losses as well as access to critical services. Results indicate that floods can lead to edge losses of up to 18%, and not only large cities but also some small cities can experience significant vulnerability to flooding. Some new or reconstructed bridges have failed to operate during analyzed flood events. During the 100 and 500-year flood return periods, the total number of inaccessible bridges within the selected cities is 184 and 294, respectively. Our work found that the shortest path length to the closest critical amenity under baseline condition can flip to the second or higher-orders during flooding. Many critical amenities have been found at risk of flooding in the studied cities.

Road Extraction in SAR Images Using Ordinal Regression and Road-Topology Loss

The road extraction task is mainly composed of two subtasks, namely, road detection and road centerline extraction. As the road detection task and road centerline extraction task are strongly correlated, in this paper, we introduce a multitask learning framework to detect roads and extract road centerlines simultaneously. For the road centerline extraction problem, existing works rely either on regression-based methods, or classification-based methods. The regression-based methods suffer from slow convergence and unsatisfactory local solutions. The classification-based methods ignore the fact that the closer the pixel is to the centerline, the higher our tolerance for its misclassification. To overcome these problems, we first convert the road centerline extraction problem into the problem of discrete normalized distance label prediction, which can be resolved by training an ordinal regressor. For the road extraction task, most of the previous studies apply pixel-wise loss function, for example, Cross-Entropy loss, which is not sufficient, as the road has special topology characteristics such as connectivity. Therefore, we propose a road-topology loss function to improve the connectivity and completeness of the extracted road. The road-topology loss function has two key characteristics: (i) The road-topology loss function combines road detection prediction and road centerline extraction prediction to promote the two subtasks to each other by using the correlation between the two subtasks; (ii) The road-topology loss can emphatically penalize gaps that often appear in road detection results and spurious segments that easily appear in centerline extraction results. In this paper, we select the AdamW optimizer to minimize the road-topology loss. Since there is no public dataset, we build a road extraction dataset to evaluate our method. State-of-the-art semantic segmentation networks (LinkNet34, DLinkNet34, DeeplabV3plus) are used as baseline methods to compare with two kinds of method. The first kind of method modifies the baseline method by adding the road centerline extraction task branch based on ordinal regression. The second kind of method uses the road topology loss and has the same network architecture as the first kind of method. For the road detection task, the two kinds of methods improve the baseline methods by up to 3.51% and 11.98% in IoU metric on our test dataset, respectively. For the road centerline extraction task, the two kinds of methods improve the baseline methods by up to 8.22% and 10.9% in the Quality metric on our test dataset.

Discovering the homogeneous geographic domain of human perceptions from street view images

Human perception of place refers to residents' psychological feelings about urban areas. Many studies of human perceptions have focused on a specific geographic location. Whether the distribution of human perceptions in continuous city space shows specific characteristics and how to disclose these phenomena remains a direction worth exploring. Due to cities' heterogeneity, quantitatively identifying the homogeneous perception regions at a fine scale within large urban regions is challenging. This study proposed a novel method to discover the homogeneous geographic domain of human perception using massive street view images. First, human perceptions of the urban visual environment were evaluated using street view images. Next, perception network models were constructed based on the road network and perception assessment results. Then, the Infomap community detection algorithm was used to identify homogeneous human perception communities. The qualitative and quantitative results verified our approach's effectiveness for capturing human perceptions' homogeneous geographic domain. Moreover, driving factor analysis was conducted to determine the urban function that may cause a community to be perceived differently based on point-of-interest (POI) data. In general, our method for combining human perceptions and the topology of urban roads could identify the homogeneous perception domain, which is valuable for urban structure studies and human perception assessment.

Success Probability Analysis of Cooperative C-V2X Communications

In this paper, we present the success probability analysis of cooperative cellular vehicle-to-everything (C-V2X) communications, i.e., cellular-relay V2X communications. We model the spatial layout of macro base stations (MBSs) as a 2D Poisson point process (PPP) and roads as a Poisson line process (PLP), with road wireless nodes (including vehicles and roadside units) modeled as a 1D PPP on each road. For a typical source node, we calculate the signal-to-interference ratio (SIR)-based success probability of transmitting a packet to its closest destination node with the assist of its nearest MBS. We take into account three cooperative transmission schemes and derive their expressions of joint success probability in two consecutive phases considering the correlation of road topology and nodes’ locations, respectively. We verify the accuracy of our analytical results through Monte-Carlo simulations. In addition, we explore the impacts of several key parameters on the success probability and discuss the selection of transmission schemes.

Fuzzy logic based forwarder selection for efficient data dissemination in VANETs

A Vehicular Ad hoc Network is a model of mobile Ad hoc network for running vehicles in which vehicles can communicate with wireless communication devices within a confined geographical region in the absence of fixed infrastructure. Also, it works as an intelligent system for road safety and traffic management by disseminating information among moving vehicles with the wireless sensor Ad hoc network, which has so many challenges and prospects since routing protocols are semi-organized for vehicular movements and constraints of road topology with rules within the limited physical connectivity. A successful broadcast is a challenging task within limited resources. The availability of the number of nodes in a range is an obstacle for rebroadcasting, can be resolved by reducing the figure with a fuzzy logic-based forwarder selection method. Signal strength can be improved in a transmission range by employing an optimum number of broadcasting points with less delay, fewer hop counts, and a high packet dissemination ratio. This optimal fuzzy broadcast algorithm is proposed for the selection of the best forwarder nodes with the consideration of inter-vehicle: distance, mobility, and density into account. Also, the performance of inter-vehicular geographic routing in high speed dynamic vehicular environment is proved with the MATLAB software, OMNeT++ tool, and SUMO environment.

Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks

With the rapid development of the Internet of vehicles (IoV), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. Due to the features of the dynamic network structure, constraints of road topology and variable states of vehicle nodes, VANET routing protocols face many challenges, including intermittent connectivity, large delay and high communication overhead. Location-based geographic routing is the most suitable method for VANETs, and such routing performs well on paths with an appropriate vehicle density and network load. We propose an intersection-based V2X routing protocol that includes a learning routing strategy based on historical traffic flows via Q-learning and monitoring real-time network status. The hierarchical routing protocol consists of two parts: a multidimensional Q-table, which is established to select the optimal road segments for packet forwarding at intersections; and an improved greedy strategy, which is implemented to select the optimal relays on paths. The monitoring models can detect network load and adjust routing decisions in a timely manner to prevent network congestion. This method minimizes the communication overhead and latency and ensures reliable transmission of packets. We compare our algorithm with three benchmark algorithms in an extensive simulation. The results show that our algorithm outperforms the existing methods in terms of network performance, including packet delivery ratio, end-to-end delay, and communication overhead.

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase

Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields

Road extraction from remote sensing images is of great significance to urban planning, navigation, disaster assessment, and other applications. Although deep neural networks have shown a strong ability in road extraction, it remains a challenging task due to complex circumstances and factors such as occlusion. To improve the accuracy and connectivity of road extraction, we propose an inner convolution integrated encoder-decoder network with the post-processing of directional conditional random fields. Firstly, we design an inner convolutional network which can propagate information slice-by-slice within feature maps, thus enhancing the learning of road topology and linear features. Additionally, we present the directional conditional random fields to improve the quality of the extracted road by adding the direction of roads to the energy function of the conditional random fields. The experimental results on the Massachusetts road dataset show that the proposed approach achieves high-quality segmentation results, with the F1-score of 84.6%, which outperforms other comparable “state-of-the-art” approaches. The visualization results prove that the proposed approach is able to effectively extract roads from remote sensing images and can solve the road connectivity problem produced by occlusions to some extent.

Optimal Adaptive Data Dissemination Protocol for VANET Road Safety Using Optimal Congestion Control Algorithm

Introduction: VANET is a mobile ad-hoc system (MANET) that concerns vehicle-to-vehicle and vehicle-to-infrastructure communication. Unique characteristics such as higher node speeds and restricted road topology extricate VANET from other MANETs. Messages between vehicles and roadside infrastructure in VANET is over short to medium range wireless technologies. Dedicated Short Range Communications (DSRC) is a RF technology-based standard that has been designed exclusively for automotive communications. Like most MANETs, data are broadcasted in VANETs through the exchange of messages between the nodes. The limited road topology unlike MANETs, executes a directional way to the message flow. It becomes important that the data to be transmitted in the most effective ways with less delay, due to higher node speeds and unbalanced connectivity among the nodes. Hence, propagating data to the intended node or region of interest is a unique problem in VANETs and requires incorporating effective techniques to disseminate data. Data broadcasted from a vehicle will be received by all the nodes within the broadcast range. The difficulty of data dissemination is hence related to propagating data, not within but beyond the transmission range of the sender. An intuitively simple way to disseminate data beyond the transmission range is through flooding. In flooding each node which receives the message would simply rebroadcast the message without any regard to its current position or any other factors. Thus, data are propagated beyond the transmission range when a node at the border of the broadcast range rebroadcasts the message. For effective broadcasting, each vehicle upon receiving a message would make a decision to rebroadcast the message depending on whether or not it is the farthest node in the transmission range. Thus, the decision-making ability of each vehicle on participating in the message propagation is dependent on its awareness of vehicles around it and determines the overall effectiveness of the technique used in disseminating data. Objective: To identify an optimal cluster head based on effective parameters to reduce Control Overhead messages in a collision based traffic network. Methods: The proposed system consists of two processes namely candidate selection and control OH message reduction. The candidate selection process is carried out by Chaotic Fish Swarm Optimization (CFSO) algorithm which consists of cluster formation and Cluster Head (CH) selection. The control OH messages are reduced by a Predictor based Decision Making (PDM) algorithm. Results: Based on the performance metrics such as success rate, redundant rate, collision rate, number of control OH messages, data propagation distance, data propagation time and data dissemination efficiency, the proposed system is evaluated. The results shows that the proposed system performs well than the existing system. Conclusion: In this paper, we have suggested an Optimal Adaptive Data Dissemination Protocol (OAddP) for VANET road safety. The proposed OAddP mechanism uses the Chaotic Fish Swarm Optimization (CFSO) algorithm to perform the clustering and uses a Predictor based Decision Making (PDM) algorithm for control overhead messages reduction.

A Map Inference Approach Using Signal Processing from Crowd-sourced GPS Data

The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. We explore map inference using a three-stage approach, which incorporates a novel Multi-source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first use of estimation theory for map inference. In particular, our approach addresses the unique challenges of vehicular GPS data. This data is plentiful but suffers from noise in location and variable coverage of regions. This makes it difficult to differentiate between noise and sparsely covered regions when increasing coverage and reducing noise. Due to the asynchronous, variable sampling rate, and often under-sampled nature of the data, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. We evaluated our method for map inference by comparing to Open Street Map maps as ground truth. We use the F-Measure, Precision, and Recall metrics to evaluate our method on Tsinghua University’s Beijing Taxi Dataset and Shanghai Jiao Tong University’s SUVnet Dataset. On these datasets, we obtained a mean<?brk?> F-Measure, Precision, and Recall of 0.7212, 0.9165, and 0.6021, respectively, outperforming a well-known method based on Kernel Density Estimation in terms of these evaluation metrics.

Traffic Message Channel Prediction Based on Graph Convolutional Network

With the development of big data, large-scale traffic flow forecasting which is a part of smart transportation has become an increasingly important research direction. Accurate and real-time traffic flow prediction is the key and difficult part of the traffic. The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle constitute a complex time correlation. For the current mainstream model, there are two main limitations: 1. Most of the existing models only focus on time correlation and ignore spatial correlation. 2. Even if the spatial correlation is concerned, the topological relationship between spaces is not fully considered. This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM). Specifically, PCA is used to reduce the dimension of data, GCN is used to learn the network topology of urban roads, LSTM is used to capture the time correlation of traffic flow. By comparing the results of different models, the proposed model is better than the current mainstream models.

DiResNet: Direction-Aware Residual Network for Road Extraction in VHR Remote Sensing Images

The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intra-class variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this paper, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) An asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) A pixel-level supervision of local directions to enhance the embedding of linear features; 3) A refinement network to optimize the segmentation results (DiResRef). Ablation studies on two benchmark datasets (the Massachusetts dataset and the DeepGlobe dataset) have confirmed the effectiveness of the presented designs. Comparative experiments with other approaches show that the proposed method has advantages in both overall accuracy and F1-score. The code is available at: https://github.com/ggsDing/DiResNet.

A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks

ABSTRACT Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks.

Infrastructure-Less Vehicle Traffic Density Estimation via Distributed Packet Probing in V2V Network

In this paper, we address the problem of vehicle traffic density estimation without relying on infrastructure cameras or sensors on the road. Previous infrastructure-less approaches still require some prior knowledge on the road infrastructure, e.g., via road topology map. We seek a lightweight estimation method based only on vehicle-to-vehicle (V2V) communication, i.e., without using any prior knowledge. The main objective of this paper is to examine traffic density through simple yet efficient packet probing within a survey time period and obtain a snapshot of the traffic density distribution map. We propose an on-demand vehicle sampling algorithm that makes a probing packet at a vehicle (i.e., sampler) keep sampling to explore the local traffic density on a cell basis. If a current sampler does not operate as an efficient carrier, the packet selects another one as the next sampler via inner-relaying and outer-relaying procedures. To effectively adapt the level of granularity of traffic density depending on the remaining survey time, we present an adaptive cell sizing algorithm. Further, we extend the sampling activity to multiple vehicle samplers by making them aggregate their collected information and also negotiate their future areas to explore. Within a designated deadline, multiple samplers collaborate for more accurate and fast traffic density estimation. By doing so by iterations till the given survey deadline, we can gather a complete view of traffic density estimates based on multiple sources where some areas have more detailed information, whereas others do less. Experiments with a real trace-driven simulation demonstrate that our proposed algorithm effectively estimates the distribution of traffic density considering local traffic conditions compared to other counterpart algorithms, with a factor of up to 9.5.

A Novel Generation-Adversarial-Network-Based Vehicle Trajectory Prediction Method for Intelligent Vehicular Networks

Prediction of the future location of vehicles and other mobile targets is instrumental in intelligent transportation system applications. In fact, networking schemes and protocols based on machine learning can benefit from the results of such accurate trajectory predictions. This is because routing decisions always need to be made for the future scenario due to the inevitable latency caused by the processing and propagation of the routing request and response. Thus, to predict the high-precision trajectory beyond the state of the art, we propose a generative adversarial network (GAN)-based vehicle trajectory prediction method, GAN-VEEP, for urban roads. The proposed method consists of three components: 1) vehicle coordinate transformation for data set preparation; 2) neural network prediction model trained by GAN; and 3) vehicle turning model to adjust the prediction process. The vehicle coordinate transformation model is introduced to deal with the complex spatial dependence in the urban road topology. Then, the neural network prediction model learns from the behavior of vehicle drivers. Finally, the vehicle turning model can refine the driving path based on the driver’s psychology. Compared with its counterparts, the experimental results show that GAN-VEEP exhibits higher effectiveness in terms of the average accuracy, mean absolute error, and root-mean-squared error.

Latency and Reliability of mmWave Multi-Hop V2V Communications Under Relay Selections

Timely and reliable information sharing among autonomous vehicles (AVs) provides a promising approach for reducing traffic congestion and improving traffic efficiency in future intelligent transportation systems. In this paper, we consider millimeter-wave (mmWave) based multi-hop vehicle-to-vehicle (V2V) communications to facilitate ultra-reliable low-latency information sharing among AVs. We propose a novel framework for performance analysis and design of relay selection schemes in mmWave multi-hop V2V communications, while taking into account the mmWave signal propagation characteristics, road topology, and traffic conditions. In particular, considering the minimum tracking distance requirement of road traffic, the headway, i.e., the distance between adjacent AVs, is modeled as shifted-exponential distribution. Moreover, we model the communication path losses using the Manhattan distance metric in the taxicab geometry, which can more accurately capture the characteristics of mmWave signal propagation in urban grid roads than conventional Euclidean distance geometry. Based on the proposed model, we investigate the latency and reliability of mmWave multi-hop V2V communications for three widely adopted relay selection schemes, i.e., random with forward progress (RFP), most forward with fixed radius (MFR), and nearest with forward progress (NFP), respectively. Furthermore, we propose a novel relay selection scheme for joint optimization of the single-hop forward progress (FP) and single-hop latency according to the AVs' instantaneous locations and an estimate of the residual multi-hop latency. Simulation results show that, by balancing the current single-hop latency and the residual multi-hop latency for the multi-hop V2V network, the proposed relay selection scheme significantly outperforms the MFR, NFP and RFP in both multi-hop transmission latency and reliability of mmWave V2V communications.

Topology dependence of on-demand ride-sharing

Traffic is a challenge in rural and urban areas alike with negative effects ranging from congestion to air pollution. Ride-sharing poses an appealing alternative to personal cars, combining the traffic-reducing ride bundling of public transport with much of the flexibility and comfort of personal cars. Here we study the effects of the underlying street network topology on the viability of ride bundling analytically and in simulations. Using numerical and analytical approaches we find that system performance can be measured in the number of scheduled stops per vehicle. Its scaling with the request rate is approximately linear and the slope, that depends on the network topology, is a measure of the ease of ridesharing in that topology. This dependence is caused by the different growth of the route volume, which we compute analytically for the simplest networks served by a single vehicle.

Kalman Filter for Local Optimum Problem of Geographic Routing Protocol in VANETs

Vehicular communication has diverse set of applications and has procured ubiquity in automotive industry these days. To perpetuate robust communication among vehicles amid progressively changing street topology is one of the difficulties of vehicular communication. Traffic density and its estimated progress information will assist in combating the urban vehicular difficulties. Although geographic routing renders best route selection for vehicles in VANET environment but it has few constraints like Local optimum problem. To resolve this problem, the paper presents a novel approach to predict proper location of vehicle in future wielding Kalman filter. Also, the real world scenario of Bengaluru city is simulated by SUMO tool for VANET network. Thus, Kalman filter precisely estimates the future location of vehicle and assists in proper information dissemination in VANETs wielding MATLAB and SUMO tool together.