Approximate algorithms for the discrete network design problem

The discrete network design problem is one of finding a set of feasible actions (projects) from among a collection of possible actions, that when implemented, optimizes some objective function(s). This is a combinatorial optimization problem that is very expensive to solve exactly. This paper proposes two algorithms for obtaining approximate solutions to the discrete network design problem with much less computational effeort. The computational savings are achieved by approximating the original problem with a new formulation which is easier to solve. The first algorithm proposed solves this approximate problem exactly, while the second is even more efficient, but provides only a near-optimal solution to the approximate problem. Experience with test problems indicates that these approximations can reduce the computational effort by a factor of 3–5, with little loss in solution accuracy.     

A latent class joint mode and departure time choice model for the Greater Toronto and Hamilton Area

This paper presents a closed-form Latent Class Model (LCM) of joint mode and departure time choices. The proposed LCM offers compound substitution patterns between the two choices. The class-specific choice models are of two opposing nesting structures, each of which provides expected maximum utility feedback to the corresponding class membership model. Such feedback allows switching class membership in response to the changes in choice contexts. The model is used for an empirical investigation of commuting mode and departure time choices in the Greater Toronto and Hamilton Area (GTHA) by using a large sample household travel survey dataset. The empirical model reveals that overall 38% of the commuters in the GTHA are more likely to switch modes than departure times and 62% of them are more likely to do the reverse. The empirical model also reveals that the average Subjective Value of Travel Time Savings (SVTTS) of the commuters in the GTHA can be as low as 3 dollars if a single choice pattern of departure time choices nested within mode choices is considered. It can also be as high as 67 dollars if the opposite nesting structure is assumed. However, the LCM estimates the average SVTTS to be around 27 dollars in the GTHA. An empirical scenario analysis by using the estimated model indicates that a 50% increase in morning peak period car travel time does not sway more than 4% of commuters from the morning peak period.

     

Telecommuting Suitability Modeling: An Approach Based on the Concept of Abstract job

A new approach to modeling telecommuting suitability is proposed in this paper. The approach, based on the concept of abstract job, can be employed to assess the level of suitability for telecommuting of the bundle of tasks comprising a job. By abstract job is meant a way of considering jobs on the basis of their elements and tasks, representing the general structure of the job. In this study, the basic tasks a job is composed of, pertaining to telecommuting suitability, are identified. To show the applicability of the approach, discrete choice models are calibrated, based on a sample of 245 employees in Tehran, Iran, indicating that from among the 6 tasks identified, 5 tasks are significantly associated with the level of telecommuting suitability.     

Network performance improvement under stochastic events with long-term effects

This paper is devoted to the problem of improving network performance to withstand incidents such as earthquakes, which have long-term adverse-effect upon the networks. A measure of link importance is presented based on consumers surplus. This measure is then used to define and solve a network improvement problem. Computational complexities are reduced by introducing an approximate measure of link importance which performs almost as good, and hence may be used for solving large scale problems. Several example networks are used to clarify the discussion numerically.     

Performance measurements of a tracked vehicle system

To improve crossing ability, the most important performance factor for tracked vehicle systems operating on low-bearing capacity peats, and to minimize income losses that result from downtime and maintenance costs, a vehicle was designed in order to adapt to operating condition changes. This article describes the mobile performance of a novel vehicle with segmented rubber tracks on a low-bearing capacity peat. At an equivalent travelling speed, the novel vehicle’s tractive performance in a variable operating environment caused by changes in terrain cohesiveness and hydrodynamic responses was superior to that of the previous model. The new vehicle, which could be operated on the Sepang peat, showed a tractive effort of 42.2% of the gross vehicle weight in field experiments; the recommended minimum tractive effort is between 30 and 36% of the gross vehicle weight.     

Nature of influence of out‐of‐vehicle time‐related attributes on transit attractiveness: a random parameters logit model analysis

This paper describes the nature of the impacts of walking distances and waiting time on transit use. The relative trade‐offs of walking and transfer components with other transit service attributes are also discussed. A total of 449 completed stated‐preference interviews were collected; with six observations from each respondent, the total number of observations was 2694. This data set was used to estimate the coefficients in different utility functions using a random parameters logit model. The results demonstrated that walking distances to and from transit stops have important and significant nonlinear negative influences on the attractiveness of transit. Transfer waiting time was also shown to have a significant nonlinear negative impact on transit attractiveness. The random parameters logit model had a better model fit than the standard logit model. Some of the findings obtained here are novel, while others are consistent with previous works. These findings have implications for both theory and practice. Copyright © 2014 John Wiley & Sons, Ltd.     

An exploratory analysis of observed and latent variables affecting intercity train service quality in developing countries

Transportation - Service quality (SQ) and customer satisfaction as perceived by 1037 passengers on intercity train services in Bangladesh were examined using structural equation modeling to explain...     

Fuzzy expert system for controlling swamp terrain intelligent air-cushion tracked vehicle

A fuzzy expert system was used in this study to control an intelligent air-cushion tracked vehicle (IACTV) as it operated in a swamp peat terrain. The system was effective in controlling the intelligent air-cushion vehicle while measuring the vehicle traction (TE), motion resistance (MR), power consumption (PC), cushion clearance height (CCH) and cushion pressure (CP). An ultrasonic displacement sensor, pull-in solenoid electromagnetic switch, pressure-control sensor, microcontroller, and battery pH sensor were incorporated into the fuzzy expert system (FES) to experimentally determine the TE, MR, PC, CCH, and CP. In this study, we provide an illustration of how an FES might play an important role in the prediction of the power consumption of the vehicle’s intelligent air-cushion system. The mean relative error in the actual and predicted values from the FES model with respect to tractive effort, total motion resistance and total power consumption were found to be 5.58 %, 6.78 % and 10.63 %, respectively. For all parameters, the relative error in the predicted values was found to be less than the acceptable limit (10%), except for the total power consumption. Furthermore, the goodness of fit of the predicted values was found to be close to 1.0 as expected and, hence, indicates the good performance of the developed system.     

Application of Ant System to network design problem

Network design problem (NDP) is the problem of choosing from among a set of alternative projects which optimizes an objective (e.g., minimizes total travel time), while keeping consumption of resources (e.g., budget) within their limits. This problem is difficult to solve, because of its combinatorial nature and nonconvexity of the objective function. Many algorithms are presented to solve the problem more efficiently, while trading-off accuracy with computational speed. This increase in speed stems from certain approximations in the formulation of the problem, decomposition, or heuristics. This study adapts a meta – heuristic approach to solve NDP, namely Ant System (AS). The algorithm is first designed, and then calibrated to solve NDP for the Sioux Falls test network. The behavior of the algorithm is then investigated. The result seems encouraging.