Mark R. McCord

Sensitivity Of Optimal Hazmat Routes To Limited Preference Specification

AbstractWe show that a multi-attribute utility (MAU) formulation of the single shipment, cost-exposure, hazardous materials optimal routing problem can conceivably be solved with traditional shortest path algorithms. We argue, however, that the parameter scaling the single attribute cost disutility function into the two attribute cost-exposure disutility function could not be estimated to more than an order of magnitude. In a numerical study using the Columbus, Ohio highway network, we see that such an estimate is not precise enough either to specify a unique optimal route or to screen out a substantial number of routes as suboptimal when the optimal route is to be other than the minimum cost or minimum risk of exposure route. The number of routes obtained when varying the parameter across its permissible range is small enough, however, that the MAU formulation can be thought of as one that generates “MAU noninferior routes” before interacting with the decision maker. We speculate on the impacts of extend...

Value of ocean current information for strategic routing

Abstract We estimate the strategic routing value of ocean currents on trans-Atlantic and trans-Pacific routes for thirty origin-destination port pairs. We do so by calculating the fuel consumption on routes strategically selected to take advantage of ocean currents relative to that on routes where the current data is ignored. The results show that exploiting currents in strategic routing could reduce the annual fuel costs of the US and world commercial fleets by

Iterative Proportional Fitting Procedure to Determine Bus Route Passenger Origin-Destination Flows

10 million and

ROUTING THROUGH DYNAMIC OCEAN CURRENTS: GENERAL HEURISTICS AND EMPIRICAL RESULTS IN THE GULF STREAM REGION

70 million, respectively. We argue that these figures should be considered lower bounds because of the coarse resolution of the available current data, resolution which should be improved with improving modeling and satellite detection systems. The figures are useful in analyzing the desirability of planned satellite missions and of private investment in dynamic current detection systems for commercial routing.

Identifying Homogeneous Periods in Bus Route Origin-Destination Passenger Flow Patterns From Automatic Passenger Counter Data

The advent of automatic passenger counter (APC) technologies is resulting in the collection of comprehensive boarding and alighting data on an ongoing basis across transit networks. The availability of APC data offers a new opportunity to determine origin–destination (O-D) flows on a frequent and comprehensive basis. In this paper, the performance of a simple procedure for route-level O-D flow determination requiring only boarding and alighting data is investigated. Specifically, the performance of the iterative proportional fitting (IPF) procedure used with a null base matrix is examined on the basis of a field experiment in which true O-D flows are observed. Because of the noninformative nature of the null matrix, using the IPF procedure with the null matrix as its input base may not be expected to produce good results. In a comparison of empirical results with those produced by other benchmark procedures, the IPF–null procedure is found to perform surprisingly well. The quality of the resulting matrices appears to be roughly similar to that of matrices derived from an onboard survey, the benchmark for what has been achieved in practice, but at much higher cost. The results indicate that much can be gained from using readily available APC data, even when the simple IPF–null procedure is applied. Moreover, using the better base obtained from an onboard survey with the IPF procedure improved performance, but less markedly compared with use of the null base; this difference indicates that combining onboard survey information with APC data provides a better O-D matrix than what can be derived from an onboard survey alone, even when the simple IPF procedure is used.

Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation

Anticipating the availability of good quality ocean current data in the near future, we formulate the problem of routing an ocean vessel through currents to minimize fuel consumption, propose methods to increase the efficiency of the solution techniques, and simulate voyages to investigate the performance of our approach. We formulate the problem as a dynamic program (DP) with two variables: Heading and Power (H&P). We then develop two heuristics, Headingthen-Power (H/P) and Heading-Alone (HA), that reduce the complexity of the formulation by decomposing the heading optimization from the power-setting optimization. To improve computational efficiency, we propose three approaches based on ship and ocean current dynamics to limit the spatial and temporal ranges that must be investigated to solve our DP formulations. In our simulation study, these approaches reduced the spatial ranges by over one third and the temporal ranges by over 70%. The study simulated minimum fuel current routing of 96 voyages in the Gulf Stream region, leading to average fuel savings of 7.4% and 4.5% for eastbound and westbound voyages, respectively. Moreover, the simplest HA heuristic, which emphasizes heading over power optimization, provided solutions as good as those provided by the most complete H&P formulation while reducing the computational time by a factor of 40. This indicates that the shipping industrys practice of emphasizing heading considerations seems appropriate in the current routing case and that current routing implementations and algorithmic developments might be able to reduce problem complexity by concentrating on spatial variables at the expense of temporal variables.

Effect of Onboard Survey Sample Size on Estimation of Transit Bus Route Passenger Origin-Destination Flow Matrix Using Automatic Passenger Counter Data

Bus passenger origin-destination (O-D) flow matrices portray information on travel patterns that can be used for route planning, design, and operations functions. Because travel patterns are known to vary throughout the day, O-D flow matrices can be expected to vary throughout the day as well. A method identifies time-of-day periods of homogeneous normalized bus route O-D passenger flow matrices in which a normalized matrix depicts the probabilities that a random passenger in the homogeneous period will travel from various origin stops to various destination stops on the route. The method uses bus trip automatic passenger counter data to estimate trip-level O-D matrices, aggregates the trip-level O-D matrices into elemental matrices for relatively short time periods, and then considers these elemental matrices as inputs to a traditional clustering procedure that is modified to ensure that a cluster indicating a period of homogeneous normalized O-D flow spans a continuous time period during the day. The homogeneous O-D flow period method is applied to empirical automatic passenger counter data collected on a bus route for which temporal travel patterns are understood. The time periods identified correspond well to the a priori understanding of travel patterns. A parallel method that uses passenger volume, rather than estimated normalized O-D flow matrices, is applied to the same data. The periods identified by this volume-based approach are not responsive to the changes in the normalized O-D flow patterns determined by the homogeneous O-D flow period identification method.

Schedule-Based and Autoregressive Bus Running Time Modeling in the Presence of Driver-Bus Heterogeneity

AbstractAn iterative method is proposed to estimate bus route origin-destination (OD) passenger flow matrices from boarding and alighting data for time-of-day periods in the absence of good a priori estimates of the flows. The algorithm is based on the widely used iterative proportional fitting (IPF) method and takes advantage of the large quantities of boarding and alighting data that are routinely collected by transit agencies using automatic passenger count (APC) technologies. An arbitrarily chosen OD matrix can be used as the base matrix required to initialize the algorithm, and the IPF method is applied with bus trip-level boarding and alighting data and the base matrix to produce an estimate of the OD flow matrix for each bus trip. The trip-level OD flow matrices are then aggregated to produce an estimate of the period-level OD flow matrix, which in turn is used as the base matrix for the following iteration. The process is repeated until convergence. Empirical results are conducted on operational b...

ESTIMATING ANNUAL AVERAGE DAILY TRAFFIC FROM SATELLITE IMAGERY AND AIR PHOTOS: EMPIRICAL RESULTS

Origin–destination (O-D) flows at the bus passenger route level have traditionally been estimated from costly and labor-intensive onboard surveys. The availability of automatic passenger counter (APC) data on many bus transit systems offers the possibility of enhancing the quality of the onboard survey data at little marginal cost. This paper investigates the value of estimating route-level passenger O-D flows from APC data and onboard O-D survey data with a focus on the effect of onboard O-D survey sample size. An empirical study using field data collected on three bus routes investigates and quantifies the value of combining APC counts with onboard O-D survey data as a function of survey sample size. Encouraging estimation performance obtained in using APC data with no onboard O-D survey data, previously seen in a smaller study, is confirmed in this more extensive study. In addition, incorporating onboard O-D survey data with APC data produces O-D flow estimates that are better than those produced by using only the APC data, and increasing the sample size of the onboard O-D survey improves the quality of these estimates. However, the magnitude of the improvement depends on the O-D flow structure of the given bus route; increasing sample size results in less appreciable improvement for routes with more concentrated O-D flows than for routes with more evenly distributed O-D flows.

Using transit or municipal vehicles as moving observer platforms for large scale collection of traffic and transportation system information

Bus route running time represents a key element of transit performance. An understanding of running time behavior and the factors that influence it is essential for off-line planning and operations design purposes including fleet size planning, schedule design, and passenger travel time performance assessment. Such an understanding is also critical for realtime applications including bus operations control and passenger information systems. This paper focuses on developing models of running time and estimating them using field data. Two model structures are considered. The schedule-based model specifies the upcoming running time as a function of the most recent deviation from the schedule the bus has exhibited at the terminus. This model characterizes the situation where a late running bus attempts to catch up with the schedule and, hence, reflects an upcoming running time shorter than the target running time, and vice versa. The autoregressive model specifies the upcoming running time as a function of the most recent running time. This model characterizes one of two situations depending on the sign of the parameter estimate. On the one hand, when the most recent running time is longer than the mean, the upcoming running time would also be longer than the mean if the operation is dominated by exogenous factors that cause delays such as other traffic or weather. On the other hand, the upcoming running time would be shorter than the mean if the driver is capable of speeding up to reduce the delay in the operation. Irrespective of the model structure, the characteristics of the driver-bus pair may also influence the extent to which the upcoming running time will deviate from the target or the mean. To capture this potential heterogeneous phenomenon, the fixed effects formulation is adopted whereby driverbus pair dummy variables are included in the model. Field data are utilized in estimating the two types of models in the presence of driver-bus heterogeneity. In general, the schedule-based model is superior to the autoregressive model in describing running time behavior. Moreover, driver-bus heterogeneity is found to be a significant contributor to this behavior.