An analysis of track mechanics to improve the simulation of the ride dynamics of tracklaying vehicles; final report,

"Performed under the technical supervision of the Dynamic Simulations Laboratory, Ordnance Tank-Automotive Command, Detroit Arsenal."

Operator's manual, carrier, guided missile equipment, self-propelled : M730 (1450-00-930-8749) and M730A1 (1450-01-121-2122).

Includes index.

Operator's manual : carrier, personnel, full tracked, armored M113A2 2350-01-068-4077 carrier, command post, light, tracked M577A2 2350-01-068-4089 carrier, mortar, 107-MM, self-propelled M106A2 2350-01-069-6931 carrier, mortar, 81-MM, self-propelled M125A2 2350-01-068-4087.

Includes index.

Direct support and general support maintenance manual : carrier, cargo, tracked, 1 1/2 ton, M973 small unit support vehicle (SUSV) (NSN 2350-01-132-9099).

Includes index.

Organizational maintenance : carrier, cargo, tracked, 1 1/2 ton, M973, small unit support vehicle (SUSV) (NSN 2350-01-132-9099).

"11 March 1988"--Change no. 1.

Direct support and general support maintenance manual : transmission assembly model X1100-3B (2520-01-067-9048) and final drive assembly model X1100-3B (2520-01-073-7665).

Includes index.

Increase in particle number emissions from motor vehicles due to interruption of steady traffic flow

We assess the increase in particle number emissions from motor vehicles driving at steady speed when forced to stop and accelerate from rest. Considering the example of a signalized pedestrian crossing on a two-way single-lane urban road, we use a complex line source method to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses and show that the total emissions during a red light is significantly higher than during the time when the light remains green. Replacing two cars with one bus increased the emissions by over an order of magnitude. Considering these large differences, we conclude that the importance attached to particle number emissions in traffic management policies be reassessed in the future.

Multi-vehicles interaction graph model for cooperative collision warning system

Cooperative collision warning system for road vehicles, enabled by recent advances in positioning systems and wireless communication technologies, can potentially reduce traffic accident significantly. To improve the system, we propose a graph model to represent interactions between multiple road vehicles in a specific region and at a specific time. Given a list of vehicles in vicinity, we can generate the interaction graph using several rules that consider vehicle's properties such as position, speed, heading, etc. Safety applications can use the model to improve emergency warning accuracy and optimize wireless channel usage. The model allows us to develop some congestion control strategies for an efficient multi-hop broadcast protocol.

Use of Probe Vehicles to Increase Traffic Estimation Accuracy in Brisbane

Traffic congestion is an increasing problem with high costs in financial, social and personal terms. These costs include psychological and physiological stress, aggressivity and fatigue caused by lengthy delays, and increased likelihood of road crashes. Reliable and accurate traffic information is essential for the development of traffic control and management strategies. Traffic information is mostly gathered from in-road vehicle detectors such as induction loops. Traffic Message Chanel (TMC) service is popular service which wirelessly send traffic information to drivers. Traffic probes have been used in many cities to increase traffic information accuracy. A simulation to estimate the number of probe vehicles required to increase the accuracy of traffic information in Brisbane is proposed. A meso level traffic simulator has been developed to facilitate the identification of the optimal number of probe vehicles required to achieve an acceptable level of traffic reporting accuracy. Our approach to determine the optimal number of probe vehicles required to meet quality of service requirements, is to simulate runs with varying numbers of traffic probes. The simulated traffic represents Brisbane’s typical morning traffic. The road maps used in simulation are Brisbane’s TMC maps complete with speed limits and traffic lights. Experimental results show that that the optimal number of probe vehicles required for providing a useful supplement to TMC (induction loop) data lies between 0.5% and 2.5% of vehicles on the road. With less probes than 0.25%, little additional information is provided, while for more probes than 5%, there is only a negligible affect on accuracy for increasingly many probes on the road. Our findings are consistent with on-going research work on traffic probes, and show the effectiveness of using probe vehicles to supplement induction loops for accurate and timely traffic information.