Abstract: Clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means
algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the
distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms.
In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city thanks to its hierarchical structure.

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 develops a novel procedure to conduct a Freight Origin-Destination Synthesis (FODS) that jointly estimates the trip distribution, mode choice, and the empty trips by truck and rail that provide the best match to the observed freight traffic counts. Four models are integrated: (1) a gravity model for trip distribution, (2) a binary logit model for mode choice, (3) a Noortman and Van Es' model for truck, and (4) a Noortman and Van Es' model for rail empty trips. The estimation process entails an iterative minimization of a nonconvex objective function, the summation of squared errors of the estimated truck and rail traffic counts with respect to the five model parameters. Of the two methods tested to address the nonconvexity, an interior point method with a set of random starting points (Multi-Start algorithm) outperformed the Ordinary Least Squared (OLS) inference technique. The potential of this methodology is examined using a hypothetical example of developing a nationwide freight demand model for Bangladesh. This research improves the existing FODS techniques that use readily available secondary data such as traffic counts and link costs, allowing transportation planners to evaluate policy outcomes without needing expensive freight data collection. This paper presents the results, model validation, limitations, and future scope for improvements.

Abstract: Although attention to data fusion has undergone rapid growth since the late 1980s, there are still relatively few applications in transportation management. Most research has based fusion weight estimation on the variance of each data source, assigning high weights to low variance data, implying that low variance means high accuracy. We propose a data fusion methodology where weights are assigned in a way data variance and sensor bias are minimized, but also consistency among data sources is maximized. The proposed methodology is flexible to work with multiple data sources, with different reliability and variability, and under different traffic conditions. The inclusion of consistency is shown to be statistically significant during special events and incidents and demonstrates its validity in successfully representing changes in traffic patterns by reasonably estimating their magnitude. Results from a case study that validate this method are shown.

Abstract: In many countries, bus operators are private companies whose service has been leased by government agencies. These agencies develop service compliance indices or measures to keep track of factors such as passenger satisfaction, frequency, and regularity but do not necessarily include the objectives of the operators in the assessment. In this paper, we used slack-based measure data envelopment analysis (SBM) to investigate whether it is possible for a bus operator to be efficient (from a private perspective) and match required standards of frequency and regularity. In doing so, data collected from two major bus operators in Santiago, Chile has been used comprising 99 services. The results show that when private objectives, namely revenues, are included in the analysis, bus operators do not necessarily seek to improve the regularity of their service. Moreover, it was found that some bus services are on the efficient frontier while keeping low performance measure standards. Using the shadow prices of the models, it was also found that improving the performance measures will be hard for many bus services unless there is a significant change in factors that are not under control of the operators (i.e., number of stops, length of the route, etc.). This shows the difficulty of correctly aligning the private objectives of operators with agencies' objectives.

Abstract: Compulsory trips (e.g., work trips) contribute with the major part of the congestion in the morning peak. It also prevents the society to reach a social optimum (the solution that maximizes welfare) because the presence of the private utility of one the agents (the firm), acting as a dominant agent, does not account for the additional costs imposed in their workers (congestion) as well as the costs imposed to the rest of the society (i.e., congestion, pollution). In this paper, a study of a strategy to influence the demand generator by relaxing the arrival constraints is presented. Bi-level programming models are used to investigate the equilibrium reached from the firm-workers interplay which helps to explain how the market failure arises. The evaluation includes the use of incentives to induce the shift to less congested periods and the case of the social system optimum in which a planner objective is incorporated as a third agent usually seeking to improve social welfare (improve productivity of the firm while at the same time reduce the total system travel time). The later is used to show that it is possible to provide a more efficient solution which better off society. A numerical example is used to (1) show the nature of the market failure, (2) evaluate the social system optimum, and (3) show how a congestion tax or an optimal incentive can help to correct the market failure. The results also corroborate that these mechanisms are more likely to be more efficient when firms face little production effects on time and workers do not high opportunity costs for starting at off peak periods.