Towards Improved Vehicle Arrival Time Prediction in Public Transportation: Integrating SUMO and Kalman Filter Models

Accurate bus arrival time prediction is a key component for improving the attractiveness of public transport. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The arrival time will be predicted using a Kalman Filter (KF) model, by utilising information acquired from social networks. Social Networks feed road traffic information into the model, based on information provided by people who have witnessed events and then updated their social media accordingly. In order to accurately assess the efficiency of KF model, we simulate realistic road scenarios using the traffic simulator Simulation in Urban Mobility (SUMO). SUMO is capable of simulating real world road traffic using digital maps and realistic traffic models. This paper discusses modelling a road journey using Kalman Filters and verifying the results with a corresponding SUMO simulation. As a second step, SUMO based measures are used to inform the KF model. Integrating the SUMO measures with the KF model can be seen as an initial step to verifying our premise that realtime data from social networks can eventually be used to improve the accuracy of the KF prediction. Furthermore, it demonstrates an integrated experimental environment

Intelligent Traffic Light Management using Multi-Behavioral Agents

XIII Jornadas de Ingeniería Telemática, 27/09/2017-29/09/2017, Valencia, EspañaOne of the biggest challenges in modern societies is to solve vehicular traffic problems. In this scenario, our proposal is to use a Multi-Agent Systems (MAS) composed of three types of agent: traffic light management agents, traffic jam detection agents, and agents that control the traffic lights at an intersection. This third type of agent is able to change its behaviour between what we have called a selfish mode (the agent will try to influence the other neighbour agents of its type to achieve its goal) or an altruistic mode (the agent will take into consideration the other neighbour selfish agents indications). To validate our solution, we have developed a MAS emulator which communicates with the Simulation of Urban MObility (SUMO) traffic simulator using the Traci tool to realize the experiments in a realistic environment. The obtained results show that our proposal is able to improve other existing solutions such as conventional traffic light management systems (static or dynamic) in terms of reduction of vehicle trip duration

zCap: a zero configuration adaptive paging and mobility management mechanism

Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards

Realistic urban traffic simulation as vehicular Ad-hoc network (VANET) via Veins framework

These days wireless communication has impacted our daily lives. The developments achieved in this field have made our lives amazingly simpler, easier, convenient and comfortable. One of these developments has occurred in Car to Car Communication (C2CC). Communication between cars often referred to vehicular ad hoc networks (VANET) has many advantages such as: reducing cars accidents, minimizing the traffic jam, reducing fuel consumption and emissions and etc. For a closer look on C2CC studies the necessity of simulation is obvious. Network simulators can simulate the Ad-hoc network but they cannot simulate the huge traffic of cities. In order to solve this problem, in this paper we study the Veins framework which is used to run a Traffic (SUMO) and a Network (OMNET++) simulator in parallel and we simulate the realistic traffics of the city of Cologne, Germany, as an ad-hoc network

Efficient Vehicular Crowdsourcing Models in VANET for Disaster Management

International audienceRoute planning in a vehicular network is a well known problem. Static solutions for finding a shortest path have proven their efficiency, however in a dynamic network such as a vehicular network, they are confronted to dynamic costs (travel time, consumption, waiting time, ...) and time constraints (traffic peaks, ghost traffic jam, accidents ...). This is a practical problem faced by several services providers on traffic information who want to offer a realistic computation of a shortest path. This paper propose a model based on the communication between vehicles (Vehicle to Vehicle: V2V) to reduce the time spend by travels taking into account the travel time registered and exchanged between vehicles in real time. In our model, vehicles act as ants and they choose their itineraries thanks to a pheromone map affected by the phenomenon of evaporation. The presented algorithms are evaluated in real world traffic networks and by modeling and simulating extreme cases such as accidents, act of terrorism and disasters

Prediction of Traffic Flow via Connected Vehicles

We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion. We anticipate flow at future time frames on a target road segment based on historical flow data and innovative features such as real time feeds and trajectory data provided by Connected Vehicles (CV) technology. To cope with the fact that existing approaches do not adapt to variation in traffic, we show how this novel approach allows advanced modelling by integrating into the forecasting of flow, the impact of the various events that CV realistically encountered on segments along their trajectory. We solve the STP problem with a Deep Neural Networks (DNN) in a multitask learning setting augmented by input from CV. Results show that our approach, namely MTL-CV, with an average Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to single task learning with Artificial Neural Network (ANN), ANN had a lower performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical similarities between segments, in contrast to using direct historical trends in the measure, because trends may not exist in the measure but do in the similarities

Adjacent Channel Interference Aware Joint Scheduling and Power Control for V2V Broadcast Communication

IEEE This paper proposes scheduling and power control schemes to mitigate the impact of both co-channel interference (CCI) and adjacent channel interference (ACI) on direct vehicle-to-vehicle broadcast communication. The objective is to maximize the number of vehicles that can communicate with the prescribed requirement on latency and reliability. The joint scheduling and power control problem is formulated as a mixed Boolean linear programming (MBLP) problem. A column generation method is proposed to reduce the computational complexity of the joint problem. From the joint problem, we formulate a scheduling-alone problem (given a power allocation) as a Boolean linear programming (BLP) problem and a power control-alone problem (given a schedule) as an MBLP problem. The scheduling problem is numerically sensitive due to the high dynamic range of channel values and adjacent channel interference ratio (ACIR) values. Therefore, a novel sensitivity reduction technique, which can compute a numerically stable optimal solution at the price of increased computational complexity, is proposed. Numerical results show that ACI, just as CCI, is a serious problem in direct vehicle-to-vehicle (V2V) communication due to near-far situations and hence should not be ignored, and its impact can be reduced by proper scheduling and power control