Planning & Scheduling Applications in Urban Traffic Management

Local authorities that manage traffic-related issues in urban areas have to optimise the use of available resources, in order to minimise congestion and delays. In this context, Automated Planning and Scheduling can be fruitfully exploited, in order to provide dynamic plans that help managing the urban road network. In this paper we provide a review of existing planning and scheduling approaches that have been designed for dealing with different aspects of traffic management, with the aim of gaining insights on the limits of current applications, and highlighting the open challenges

Pre-Timed and Coordinated Traffic Controller Systems Based on AVR Microcontroller

The major weaknesses of traffic controllers in Indonesia are unable to accommodate the variety of traffic volume and unable to be coordinated. To solve the problem, a pre-timed and coordinated traffic controller system is build. The system consists of a master and a local controller. Each controller has a database containing signal-timing plans that would be allocated to manage vehicle flows. To synchronize the signal-timing, the master controller sends the synchronization data to the local controller wirelessly and the local controller shifts the end of a cycle by adding or subtracting the green interval of any phases. The transition time for synchronization only takes one to several cycles. The algorithm for controlling the traffic including coordination can be done by an AVR microcontroller. Memory usage of the microcontroller is lower than 10% meanwhile the CPU utilization is no more than 1%, thus the systems could be widely developed

Online traffic signal control through sample-based constrained optimization

Ministry of Education, Singapore under its Academic Research Funding Tier

An Algorithm for Calculating the Inverse Jacobian of Multirobot Systems in a Cluster Space Formulation

Multirobot systems have characteristics such as high formation re-configurability that allow them to perform dynamic tasks that require real time formation control. These tasks include gradient sensing, object manipulation, and advanced field exploration. In such instances, the Cluster Space Control approach is attractive as it is both intuitive and allows for full degree of freedom control. Cluster Space Control achieves this by redefining a collection of robots as a single geometric entity called a cluster. To implement, it requires knowing the inverse Jacobian of the robotic system for use in the main control loop. Historically, the inverse Jacobian has been computed by hand which is an arduous process. However, a set of frame propagation equations that generate both the inverse position kinematics and inverse Jacobian has recently been developed. These equations have been used to manually compile the inverse Jacobian Matrix. The objective of this thesis was to automate this overall process. To do this, a formal method for representing cluster space implementations using graph theory was developed. This new graphical representation was used to develop an algorithm that computes the new frame propagation equations. This algorithm was then implemented in Matlab and the algorithm and its associated functions were organized into a Matlab toolbox. A collection of several cluster definitions were developed to test the algorithm, and the results were verified by comparing to a derivation based technique. The result is the initial version of a Matlab Toolbox that successfully automates the computation of the inverse Jacobian Matrix for a cluster of robots

Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

Improving a city’s infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators

Schedule-Driven Coordination for Real-Time Traffic Network Control

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation