Freight Demand Estimation from Secondary Sources

摘要

This report explains how to estimate freight demand using secondary source of data such as traffic counts. Freight origin-destination (OD) matrices are one of the most important data elements a planner could have, which is why a significant amount of effort, time and money is spent on their estimation. The estimation of OD matrices can be done by: (a) direct sampling methods; and, (b) using secondary data sources such as traffic counts. The latter techniques are referred to here as origin-destination synthesis (ODS).OD data are obtained by interviewing the participants in the transportation activity and have some well-known limitations: roadside interviews tend to double count trips; on-board interviews may lead to bias in the parameters of random utility models; mail interviews are often biased because the rate of response varies across the population; and home interviews, though able to provide statistically sound estimates of OD, require a great deal of planning, time, effort and money (Ortuzar and Willumsen, 2001).The proposed ODS procedure permits the estimation of freight OD matrices using secondary sources in Manhattan. The secondary data sources consist of truck traffic counts from 97 intersections in Midtown Manhattan provided by the NYSDOT. The framework developed here will enable NYSDOT, NYMTC and other transportation management agencies to estimate freight OD matrices from traffic counts at a much reduced cost and with relative good accuracy. The framework will also make it possible to seamlessly integrate freight planning into agencies transportation system planning. In ODS, the traffic countswhich are a function of the OD flowsare used to estimate the OD matrices. Since the number of unknowns (OD pairs) exceeds the number of independent traffic counts, the estimation problem is under-specified. This requires the use of analytical techniques to estimate the most likely OD matrix that fits the observed traffic counts. The research on ODS has concluded that, though not a replacement for actual data, it could produce fairly realistic estimates of freight OD matrices. This, in turn, could play a significant role in boosting demand modeling efforts as collecting freight data is extremely time consuming and expensive. The results of the proposed ODS model could be improved by implementing a multi-path algorithm to assign traffic.