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.     

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.     

美国桥梁技术近期发展概况

美国是公认的世界上交通运输网最广泛的国家.随着74 674 km国道系统的建成,并伴随着人们对于旅游和货物运输要求的持续增长,交通便利性已经成为了一种权利.全国统计在册的600 000座桥梁的平均桥龄为45年,这就为推进及推广应用桥梁技术的研究提供了巨大的机会,通过这些技术,可使公路基础设施更新中的桥梁更耐久、更经济,可采用更快速的施工技术及修建更可靠、更便于维护的结构.介绍美国桥梁技术近期发展概况.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

Income-contingent user preferences in policy evaluation: application and discussion based on multi-agent transport simulations

Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure.