Abstract: The paper develops analytical formulations to gain insight into the optimal location, i.e., the one that maximizes the potential market, and to estimate the potential catchment area of Park and Ride (P&R) facilities. The formulations are based on the assumption that a traveler would use a P&R facility if and only if the corresponding generalized cost is lower than the drive only alternative. The paper considers two different scenarios: a linear city (or a travel corridor), and a two-dimensional city with Euclidean travel. Analytical derivations were obtained for both cases using, as starting point, the necessary condition for P&R use. In the case of the linear city, the paper identifies two breakeven distances (BEDs) of great import to the estimation of the potential P&R market: the (trip) origin BED, i.e., the distance below which a traveler could drive upstream to use the P&R facility to access its downstream destination, and still be better off; and the (trip) destination BED, i.e., the travel distance using transit below which it does not make sense to use P&R. The paper proves that the optimal location of P&R sites is shifted upstream of what seems to be an intuitive solution, i.e., the edge of the congested region. by a distance that depends on the relative values of the origin and destination BEDs. In the two-dimensional city case, the analytical derivations prove that, for a given trip from i to j, the set of feasible locations follows an ellipse-like figure with the trip origin as a focus. These shapes-referred to as limiting functions-depend on variables such as trip distance, transit level of service (LOS), and the like. The analyses indicate that the area enclosed by the limiting functions increases with the transit LOS and trip distance, and so do the corresponding catchment areas. This is because the catchment area is determined by the marginal trip origins, i.e., those for which the P&R facility is just inside the limiting function. In its final section, the paper develops a parabolic approximation to the catchment area for a given P&R site. The approximating parabola is defined by three critical points: the origin BED, and two points that identify the marginal trip origins at the chord of parabola evaluated at the P&R. The numerical experiments indicate that the parabolic approximation provides a fairly good estimate of the catchment area that is easy to produce, conceptually valid, and overcomes the limitations of alternative approaches and rules of thumb used by practitioners and researchers. (c) 2012 Elsevier Ltd. All rights reserved.

Abstract: This paper uses a comprehensive experimental design to investigate the influence of various traffic count sampling plans for Bangladesh on the performance of the Freight Origin-Destination Synthesis model with Mode Choice (FODS-MC) developed by Kalahasthi et al. FODS-MC estimates a national-level freight demand model including trip distribution, mode choice, empty truck trips, and empty rail trips, where one of the key inputs is the freight truck and rail, traffic counts. The traffic count sample comprises three types of road links (national, regional, and zilla) and one category for the rail link across the country. A Box-Behnken Design (BBD) with a response surface for each of four FODS-MC parameters (distribution, mode choice, truck empty trips, and rail empty trips) is constructed. The results showed that the response surfaces are nonlinear planes for all parameters. There is no single optimal sampling plan for FODS-MC as each model parameter demands different distribution across the truck and rail links. The random and stratified samples perform almost similarly if less than 20% of the sample is collected. Minimizing the loss functions between the estimated and true parameters shows that a random sample between 20% and 25% of the truck and rail links estimates the best freight demand model. Overall, this research develops a framework to assist public practitioners in the optimum usage of the limited time and resources in collecting the traffic count data that could estimate the freight demand and mode choice models effectively.

Abstract: Traditional or standard work schedules refer to the requirement that workers must be at work the same days and during the same hours each day. This requirement constrains work-related trip arrivals, and generates morning and afternoon peak hours due to the concentration of work days and/or work hours. Alternative work schedules seek to reschedule work activities away from this traditional requirement. The aim is to flatten the peak hours by spreading the demand (i.e., assigning it to the shoulders of the peak hour), lowering the peak demand. This not only would reduce societal costs but also can help to minimize the physical requirements. In this article, a two-stage optimization model is presented to quantify the effects of staggered work hours under incentive policies. In the first stage, a variation of the generalized quadratic assignment problem is used to represent the firm's assignment of workers to different work starting times. This is the input of a nonlinear complementarity problem that captures the behavior of the users of the transportation network who are seeking to overcome the constraints imposed by working schedules (arrival times). Two examples are provided to show how the model can be used to (a) quantify the effects and response of the firm to external incentives and (b) evaluate what type of arrangements in starting times are to be made in order to achieve a social optimum.

Abstract: Flextime is a Travel Demand Management policy that allows workers to arrive/leave within a certain threshold of time but staying on duty during a period of the day (core hours). Such flexibility in arrival time can have an impact on relieving traffic congestion as it can shift demand to nonpeak periods. However, in several countries, companies are usually reluctant to provide flexibility arguing possible loss in their productivity and efficiency. This paper discusses a preliminary study on perceptions of challenges, and the potential of flextime in congested urban areas. In doing so, we present the information gathered through in-depth-interviews and subsequent questionnaires applied to managers of cars dealers in Santiago, Chile, obtaining both qualitative and quantitative information. The quantitative information obtained was used to develop (1) a data envelopment analysis to investigate whether firms' productivity is affected by flextime or not, and (2) a binary logit model to evaluate financial and non-financial incentives that can allow firms to adopt flextime or change work arrival times. The results obtained from the sample show that, for this particular industry, flextime might not have a significant impact on firms' productivity. Additionally, we found that financial incentives and competitors' work starting times have a significant impact on accepting flextime or relaxing arrival constraints. We also found that if arrangements with the whole industry to change work starting times are made, the effect of this change can be as effective as the provision of financial incentives.