Empirical Study of Urban Commercial Vehicle Tour Patterns in Texas. W ei Zhou, Jane Lin University of Illinois at Chicago Department of Civil and Materials Engineering. Motivation. - PowerPoint PPT Presentation
Empirical Study of Urban Commercial Vehicle Tour Patterns and Regional Differences in Texas and Idaho
Empirical Study of Urban Commercial Vehicle Tour Patterns in TexasWei Zhou, Jane LinUniversity of Illinois at Chicago Department of Civil and Materials Engineering
SMotivationUrban commercial vehicle movements contribute to congestion, adverse environmental, social impacts and safety issues.Innovative urban logistics and policies are needed to counteract these negative effects and improve logistical performance with growing goods flows.The understanding of urban goods and commercial vehicle movements is thin because of the lack of freight data (often proprietary) and the fact that freight vehicle movements are part of the complex supply chains and logistics activities.ObjectiveTo provide an empirical investigation of urban commercial vehicle movements in five metropolitan regions - San Antonio, Amarillo, Valley, Lubbock and Austin in Texas.
To quantify how factors such as land use type, shipment demand, cargo type, loading/unloading cargo weight and travel speed affect the commercial vehicle daily trip chaining strategies.
Data DescriptionDataset: Texas Commercial Vehicle Surveys in five counties of San Antonio, Amarillo, Valley, Lubbock and Austin during 2005 and 2006.Drivers or operators of the sampled vehicles completed both a vehicle information form and a daily travel log on an assigned day. The vehicle information form contains basic vehicle data like vehicle type, fuel type, odometers, etc.; The travel log records all trips the commercial vehicle made and all locations they visited during the study day.
Common Key Variables In The DatasetStop-level attributes:Longitude and latitudeDeparture/arrival time at stopsLoading/unloading cargo type Loading/unloading cargo weightActivity type Land use type
Description of Individual ToursTwo basic types of urban commercial vehicle tours:
Direct tour: A direct tour consists of only one stop/visit to a customer before returning to the base;
Peddling tour: A peddling tour consists of multiple stops/visits to customers before returning to the base.
Tour Choice ModelsMultinomial logit (MNL) model is built for Texas data with four alternative tour choices: - Direct tour;- Peddling tour with two customer stops; - Peddling tour with three to five customer stops;- Peddling tour with more than five customer stops.Explanatory VariablesTour length Avg. length Avg. speed Land use type Cargo type Activity type Tour travel time Dwell time Loading/Unloading cargo weight Net Cargo Drop-off Size Semi Truck Single unit truck Light duty truck Empty tripsModel ResultsNumber of Observations : 950Log-Likelihood at Constant : -1143.3422 Log-Likelihood at Convergence : -582.5896Rho-Squared w.r.t Constant : 0.4905Adjusted Rho-Squared w.r.t Constant : 0.4750Model Results (ContPeddling tour with two customer stopsPeddling tour with three to five customer stopsPeddling tour with more than five customer stopsVariablesStdDev.StdDev.StdDev.Constant-1.039***0.1006-1.0690***0.1004-1.3894***0.1149Farm Products 0.47960.6394-0.27460. 5940Food, Health, and Beauty Products1.9353***0.5961Wood Products0. 24470.8655Clay, Concrete, Glass, or Stone2.6144**1.13802.4504**1.0255Goods Pick-up From a Non-Base Location1.7789***0.39461.9655***0.41111.8051***0.5177Goods Pick-up and Delivery From a Non-Base Location1.5058**0.67491.9896***0.64141.6856**0.7577Visit to Retail-.9057** 0.3732-0.50610.3147Unloading Cargo Weight-.5418***0.1313-0.34010.1297Semi Truck-1.2558***0. 4118-3.7849***0.6179Empty Trips-0.5536***0.1287-0.1739***0.0561ConclusionsThe model outputs in this research has led to the following conclusions:Urban commercial vehicle tour strategies tend to be associated with cargo type, travel purpose, travel time, dwell time and tour destination. The limitations associated with the limitation of the survey data itself in this study requires further research:Other variables such as regional land use, urban sprawl, and road network characteristics should be included. Some economic indicators like size, revenue as well as other key logistics information such as logistics costs, time windows, vehicle capacity, and driver work hour should be incorporated. Thanks!