Project : sagep
Section: Scientific Foundations
Keywords : Data analysis , Realtime decision making system , Urban traffic , Simulation .
Urban traffic
Participants : Anjali Awasthi, Michel Parent, JeanMarie Proth.
Urban network traffic simulation
Prediction of accurate travel time plays an important role in dynamic route guidance on urban networks. The dynamic traffic flows entering the network affect the behavior of the system and the free flow movement of vehicles. To study the complex system dynamics of an urban network under varying input flows, we begin with the basic unit of the network called a lane. The first step consists in analyzing of the behavior of a single lane and computing the travel time using the characteristics of the lane, the input flow and the constraints on the output flow. The system analysis of the single lane is then applied to the whole network for studying the transfer of flows inside the network. A dynamic network model has been proposed for the network analysis. A simulation software has been developed using the dynamic network model which generates travel times in the network using the input data as the flows coming at the entrances of the system, the initial system states of the lanes and the characteristics of the network.
The second step consists of estimating fastest paths in urban networks using data analysis. The network data for statistical analysis is generated using a macroscopic traffic flow based simulation software. The input to the software are the input flows and the arc loads (the number of cars in each arc), and the outputs of the software are the various paths joining the origins and the destinations of the network.
The network data obtained from the simulation software is subjected to hybrid clustering followed by canonical correlation analysis. The hybrid clustering comprises of two methods namely Kmeans analysis and Ward's hierarchical agglomerative clustering. The results of the data analysis are decision rules containing arc loads and input flows that govern the fastest paths on the network. These rules are used for predicting the paths to follow while arriving at the entrances of the network. Before entering the network, the arc loads and input flows provided by the rules are checked inside the network. If agreement is found, then the path obtained from the data analysis is the fastest path otherwise the shortest path is chosen as the fastest path.
Real networks are considerably huge in size and the above approach can be applied to networks of relatively small dimension. The next step of our study is to develop an algorithm for approximating fastest paths on real networks. This would be done by decomposing the real network into small subnetworks and computing the fastest path for the subnetworks using the statistical approach discussed in the paper. The fastest path between any origin destination pair of the real network will be obtained by joining the average fastest paths of the subnetworks and by selecting the fastest path in realtime at the entrance of each subnetwork.
Cybercars Fleet Management
The fleet management problem consists of providing vehicles(cybercars) to customers on demand for going from an origin to a destination. These vehicles are fully automated and are driven under the supervision of a Centralized Fleet Management System (CFMS). The objective of the CFMS is to achieve maximal customer satisfaction by providing efficient service and at the same time keeping an eye on system costs. The customer satisfaction is measured in terms of minimum waiting and travel time of the customers which is ensured by optimal scheduling and routing of vehicles. The system cost is kept low by ensuring usage of minimal number of vehicles, avoiding empty movement of vehicles, and close accessibility of stops to pickup and recharging stations.
A simulation model was developed for CFMS to ensure demand responsive routing and scheduling of cybercars. Efficient customer service by CFMS is ensured by optimal performance of the following functionalities:

division of network into clusters,

request pooling and scheduling,

vehicle allotment to requests,

dynamic vehicle routing,

allocation of empty vehicles after service.