A continuous network design model in stochastic user equilibrium based on sensitivity analysis

The continuous network design problem (CNDP) is known to be difficult to solve due to the intrinsic properties of non‐convexity and nonlinearity. Such kinds of CNDP can be formulated as a bi‐level programme, in which the upper level represents the designer's decisions and the lower level the travellers' responses. Formulations of this kind can be classified as either Stackelberg approaches or Nash ones according to the relationship between the upper level and the lower level parts. This paper formulates the CNDP for road expansion based on Stackelberg game where leader and follower exist, and allows for variety of travellers' behaviour in choosing their routes. In order to solve the problem by the Stackelberg approach, we need a relation between link flows and design parameters. For this purpose, we use a logit route choice model, which provides this in an explicit closed‐form function. This model is applied to two example road networks to test and briefly compare the results between the Stackelberg and Nash approaches to explore the differences between them.     

Formal scientific research of traffic collision data utilizing GIS

This paper explores the tenuous link between speeding behavior and accident causation, one that has not been well established in the international literature to date. Taking advantage of established engineering conventions and formulae, we were able to set up an a priori hypothesis suitable for testing. Utilizing this formal scientific method (which GIS researchers have been criticised for not using) we establish a statistical link for this relationship. Our methodology can be used to support all police intervention strategies, including the controversial photo radar systems. The results from our research have been entered into a GIS in order to create a map for spatial display. This map illustrates the relative probability or risk of collision occurrence resulting from speeding at all intersections and interchanges within the scope of the study. It is suggested that this methodology could easily be maintained with periodic updates of data, thus creating a dynamic model from which to monitor traffic safety within the city. Furthermore, this model can be utilized to study specific strategies, allowing for the scrutiny of before, during and after effects. The study area is the entire city of Calgary, Alberta, Canada, and includes all traffic collisions occurring during the year of 1994.     

Location of bus garages

The location of bus garages is a complex issue that has received recent attention in the literature. Given a bus system, the number of bus garages and their locations depend on garage cost, deadheading cost and environmental impacts. An approximate analytical model is used to determine the number of bus garages that minimizes the above costs. The concept of a slowly varying density of bus-route origins (hence deadheads) per unit area is used to model deadheading costs. The increased deadheading caused by breakdowns and accidents is also considered. The garage cost is modeled as a function of the number of buses stored. A closed-form solution is obtained for the optimal density of garages, when the garage cost function is linear. The actual locations of garages and the allocations of buses to the garages are found using a discrete space location-allocation model formulated so as to consider the environmental impact associated with buses deadheading through populated neighborhoods.     

A note on the interpretation of the dual variables in estimating traffic capacity at signal- controlled road junctions

The values of the dual variables in a constrained optimisation problem can be used to estimate the sensitivity of the optimal value of the objective function to changes in the constraints. Allsop (1972) used standard methods of linear programming to derive expressions for the sensitivity of the reserve capacity at a signal-controlled road junction to various changes in the traffic engineering constraints. That analysis used the assumption that the maximum reserve capacity would be achieved when the cycle time used is maximal. While this normally occurs, some junctions have come to light where a reduction in the cycle time increases the capacity. Allsop's analysis is extended here to account for this possibility.     

A least absolute deviation critical decision path analysis with transport applications

This paper introduces a method that simultaneously analyzes travel variables from stated preferences that are measured under each of several different assumptions. The method uses least absolute deviation estimators and linear programming solutions and is flexible enough to permit inclusion of constraints for ordinal data and latent variables. Travel behavior is characterized by different indicators such as travel time, waiting time, mode choice and departure time. Consideration of different response variables simultaneously as part of a stated preference model requires a reclassification of variables as either endogenous or exogenous. This concept was introduced by the author as structural conjoint analysis earlier. Each endogenous variable may be defined as nominal, ordinal or cardinal and may be either explicitly measured or latent. Current econometric and psychometric techniques cannot accommodate this variety of data. The procedure is essentially a two-stage least absolute deviation simultaneous equation regression. The estimation technique is well known as are the various hypothesis tests. In the method each relationship between endogenous and exogenous variables is formulated separately carefully incorporating assumptions about each type of data. Thus there are different formulations for endogenous variables that are nominal and latent, ordinal and explicit, ordinal and latent, cardinal and explicit and cardinal and latent. Formulations for nominal latent, ordinal explicit and cardinal explicit variables were tested with simulated data for three separate hypothetical problems. Each problem consisted of at least two different types of variables and the technique was found to be able to reproduce the simulation function coefficients in virtually all cases.     

A linear programming approach to conjoint measurment and trade-off analysis

There has been increasing attention placed on decomposition techniques such as conjoint measurement and trade-off analysis. Existing algorithms for both approaches use non-linear estimation techniques. In this paper, a linear integration rule is proposed along with a linear programming algorithm which is used to estimate part-utilities by minimizing the sum of the absolute deviations subject to monotonicity constraints. The algorithm was tested on simulated data and empirically. It was found that the algorithm performed well, especially when data are consistent with initial assumptions.     

Capacity at a signal-controlled junction where there is priority for buses

A discussion of the benefits of bus priority schemes shows the importance of providing adequate capacity for all streams of traffic. The analysis of capacity at a signal-controlled road junction is extended to cases where stages may be truncated or omitted in some cycles. The problem of finding signal settings which, when implemented with priority, emulate some which are known to provide a given level of capacity when implemented without priority is considered. Two commonly used rules to give priority by selective vehicle detection are analysed in detail and a third is considered briefly to illustrate the flexibility of the methods used. The range of conditions under which these priority methods can be implemented without causing any loss of capacity is quantified. If an additional rule is implemented to prevent priority from being granted too frequently, then this range covers most practical operating conditions. In cases outside this range, consequent losses of capacity can be estimated. A numerical example based upon a real bus priority experiment is provided.     

Vehicle reidentification and travel time measurement on congested freeways

The paper presents an algorithm for matching individual vehicles measured at a freeway detector with the vehicles’ corresponding measurements taken earlier at another detector located upstream. Although this algorithm is potentially compatible with many vehicle detector technologies, the paper illustrates the method using existing dual-loop detectors to measure vehicle lengths. This detector technology has seen widespread deployment for velocity measurement. Since the detectors were not developed to measure vehicle length, these measurements can include significant errors. To overcome this problem, the algorithm exploits drivers’ tendencies to retain their positions within dense platoons. The otherwise complicated task of vehicle reidentification is carried out by matching these platoons rather than individual vehicles. Of course once a vehicle has been matched across neighboring detector stations, the difference in its arrival time at each station defines the vehicle’s travel time on the intervening segment.Findings from an application of the algorithm over a 1/3 mile long segment are presented herein and they indicate that a sufficient number of vehicles can be matched for the purpose of traffic surveillance. As such, the algorithm extracts travel time data without requiring the deployment of new detector technologies. In addition to the immediate impacts on traffic monitoring, the work provides a means to quantify the potential benefits of emerging detector technologies that promise to extract more detailed information from individual vehicles.