UC Irvine Institute of Transportation Studies

Trucks have a significant impact on infrastructure, traffic congestion, energy consumption, pollution and quality of life. To better understand truck characteristics, comprehensive high resolution truck data is needed. Higher quality truck data can enable more accurate estimates of GHGs and emissions, allow for better management of infrastructure, provide insight to truck travel behavior, and enhance freight forecasting. Currently, truck traffic data is collected through limited means and with limited detail. Agencies can obtain or estimate truck travel statistics from surveys, inductive loop detectors (ILD) and weigh-in-motion (WIM) stations, or from manual counts, each of which have various limitations. Of these sources, WIM and ILD seem to be the most promising tools for capturing detailed truck information. Axle spacing and weight from existing WIM devices and unique inductive signatures indicative of body type from ILDs equipped with high sampling rate detector cards are complementary data sources that can be integrated to provide a synergistic resource that otherwise does not exist in practice, a resource that is able to overcome the drawbacks of the traditional truck data collection methods by providing data that is detailed, link specific, temporally continuous, up-to-date, and representative of the full truck population. This integrated data resource lends itself very readily toward detailed truck body classification which is presented as a case study. This body classification model is able to predict 35 different trailer body types for FHWA class 9 semi-tractors, achieving an 80 percent correct classification rate. In addition to the body classification model, the large data set resulting from the case study is itself a valuable and novel resource for truck studies.

Monitoring Commerical Vehicle Activities is very important for developing and  maintaining efficient freight transport systems. In the existing Literature this is broadly done through vehicle classification and reidentification problems using various sensing technologies. Lidar is an emerging traffic sensing technology which could potentially serve as a multi functional sensor for transport systems. In out current work we mainly focused on developing and qualitatively assessing a Lidar based Reconstruction framework for Truck surveillance purpose. We proposed a two stage Truck body reconstruction framework and found the results of reconstructed Truck bodies are quite promising for several truck-trailer configurations. For certain types of Truck-Trialer configurations such as containers due to the sparsity of scanned points in lateral direction, the wheel portion of reconstructed body still has noticeable deformations. We would like to address the same in our future work.

Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years, thanks to the wide-spread use of smart phones, mobile application development platforms, and online payment systems. An assignment of drivers to riders, known as the ride-matching problem, is the central component of a peer-to-peer ridesharing system. In this paper, we discuss the features of a flexible ridesharing system, and propose an algorithm to optimally solve the ride-matching problem in a flexible ridesharing system in real-time. We generate random instances of the problem, and perform sensitivity analysis over some of the important parameters in a ridesharing system. Finally, we introduce the concept of peer-to-peer ride exchange, and show how it affects the performance of a ridesharing system.

Studies on the economic impact of air cargo traffic have been gaining traction in recent years. The slowed growth of air cargo traffic at California’s airports, however, has raised more pressing questions amongst airport planners and policy makers regarding the determinants of air cargo traffic. Specifically, it would be useful to know howCalifornia’s air cargo traffic is affected by urban economic characteristics surrounding airports. Accordingly, this study estimates the socioeconomic determinants of air cargo traffic across cities in California. We construct a 7-year panel (2003-2009) using quarterly employment, wage, population, and traffic data for metro areas in the state. Our results reveal that the concentration of service and manufacturing employment impacts the volume of outbound air cargo. Total air cargo traffic is found to grow faster than population, while the corresponding domestic traffic grows less than proportionally to city size. Wages play a significant role in determining both total and domestic air cargo movement. We provide point estimates for the traffic diversion between cities, showing that 80 percent of air cargo traffic is diverted away from a small city located within 100 miles of a large one. Using socioeconomic and demographic forecasts prepared for California’s Department of Transportation, we also forecast metro-level total and domestic air cargo tonnage for the years 2010-2040. Our forecasts for this period indicate that California’s total (domestic) air cargo traffic will increase at an average rate of 5.9 percent (4.4 percent) per year.

The solution algorithms for the family of flow distribution problems, which include (1) the trip distribution problem of travel forecasting, (2) the OD estimation from link counts problem, and (3) the trip matrix disaggregation problem, are usually based on the Maximum Entropy (ME) principle. ME-based optimization problems are hard to solve directly by optimization techniques due to the complexity of the objective function. Thus, in practice, iterative procedures are used to find approximate solutions. These procedures, however, cannot be easily applied if additional constraints are needed to be included in the problem. In this paper a new approach for balancing trip matrices with application in trip matrix disaggregation is introduced. The concept of generating the most similar distribution (MSD) instead of the Most Probable Distribution of Maximum Entropy principle is the basis of this approach. The goal of MSD is to minimize the deviation from the initial trip distribution, while satisfying additional constraints. This concept can be formulated in different ways. Two MSD-based objective functions have been introduced in this paper to replace the ME-based objective function. One is the Sum of Squared Deviations MSD (SSD-MSD), and the other is Minimax-MSD. While SSD-MSD puts more emphasis on maintaining the base year trip shares as a whole, Minimax-MSD puts more emphasis on maintaining the share of each individual element in the trip table. The main advantage of replacing the entropy-based objective functions with any of these functions is that the resulting problems can include additional constraints and still be readily solved by standard optimization engines. In addition, these objective functions could produce more meaningful results than entropy-based functions in regional transportation planning studies, as shown in the case study and some of the examples in the paper. Several examples and a case study of the California Statewide Freight Forecasting Model (CSFFM) are presented to demonstrate the merits of using MSD-based formulations.

A new facility location model and a solution algorithm are proposed that feature 1) itinerary-interception instead of flow-interception; 2) stochastic demand as dynamic service requests; and 3) queueing delay. These features are essential to analyze battery-powered electric shared-ride taxis operating in a connected, centralized dispatch manner. The model and solution method are based on a bi-level, simulation-optimization framework that combines an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation based on earlier work by Jung and Jayakrishnan. The solution algorithm is tested on a fleet of 600 shared-taxis in Seoul, Korea, spanning 603 km2, a budget of 100 charging stations, and up to 22 candidate charging locations, against a benchmark “naïve” genetic algorithm that does not consider cyclic interactions between the taxi charging demand and the charger allocations with queue delay. Results show not only that the proposed model is capable of locating charging stations with stochastic dynamic itinerary-interception and queue delay, butt that the bi-level solution method improves upon the benchmark algorithm in terms of realized queue delay, total time of operation of taxi service, and service request rejections. Furthermore, we show how much additional benefit in level of service is possible in the upper-bound scenario when the number of charging stations approaches infinity.

This paper presents a new method for estimating traffic density on freeways, and an adaptation for real-time applications. This method uses re-identified vehicles and their travel times estimated from a real-time vehicle re-identification (REID) system which attempts to anonymously match vehicles based on their inductive signatures. The accuracy of the section- 6 based density estimation algorithm is validated against ground-truth data obtained from recorded video for a six-lane, 0.66-mile freeway segment of I-405N in Irvine, California, during the morning peak period. The proposed density estimation algorithm results are compared against a g-factor based method which relies on inductive loop detector occupancy data and estimated vehicle lengths from the Caltrans Performance Measurement System (PeMS) as well as a selected REID method which uses a sparse REID algorithm based on long vehicle detection and volume counts at detector stations. Although the g-factor approach produces real-time density estimates, it requires seasonally calibrated parameters. In addition to the calibration effort to maintain overall accuracy of the system, the g-factor approach will also produce errors in density estimation if the actual composition of vehicles yields a different observed g-factor from the calibrated value. In contrast, the proposed method uses an existing vehicle re-identification model based on the matching of inductive vehicle signatures between two locations spanning a freeway section. This approach does not require assumptions on the vehicle composition, hence does not require calibration. The proposed algorithm obtained section-based density measures with a mean absolute percentage error (MAPE) of less than four percent when compared against groundtruth data and provides accurate density estimates even during congested conditions, improving both the PeMS and selected alternative REID based methods.

Freight forecasting models are data intensive and require many explanatory variables to be accurate. One problem, particularly in the United States, is that public data sources are mostly at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. Second, supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a structural equation modeling approach, which is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between commodities. A FAF-based structural commodity generation model is specified and estimated and shown to provide a better fit to the data than independent regression models for each commodity. Three features of the model are discussed: indirect effects, supply chain elasticity, and intrazonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using data imputed with a goal programming method.