Accounting for Commercial Vehicles in Urban Transportation Models

Typical vanpooler sacrifices 10 to 12 minutes of travel time compared to driving alone, trading time off against other attributes such as reduced travel cost and stress.

Vanpooling accounts for 0.2-0.3 percent of all journey to work travel nationally.

Vanpool mode share increases ranging from 70 percent to a triple tripling of usage have been reported in response to substantial or total fare subsidy.

All vanpool program trip length averages fall within the range of 26-42 miles one-way (compared to the national average of just over 10 miles for solo auto driver commute trips, and 5 miles for the average unlinked transit trip).

Vanpool passengers tend to have socioeconomic profiles more like auto commuters than transit riders.

This region leads the nation in vanpooling, providing 40% of public vanpools in the nation (with 1250 public vanpools and 200 private vanpools by 1999). There are 2% of commuters now regularly taking advantage of this commute option. For commuters who travel more than 20 miles each way, vanpool has reached a 7% market share (in comparison, transit has a 13% share). Vanpooling has continued to grow steadily ever since. It experienced a growth of more than 60% between 1995 and 1999. Growth in this system has averaged 121 vans and 1200 commuters each year.

It is estimated that public vanpools eliminate 11,000 vehicles and 22,000 trips every year; vanpools in the region reduce travel mileage of SOVs by 2.7 million miles annually.

New York City

Regression models are developed for fares, fare revenue and cab availability measured by total taxi industry mileage operated without passengers.

Revenue per mile can be used to estimate empty taxi rides.

Some what if scenarios are simulated using the estimated models.

Provide trends during the years, but in percentage changes from year to year.

Provide figures for adjusted revenue per mile that vary from $1.02 to $1.25 during these years.

Total empty taxi miles vary from 80-105 Million during these years.

A unique time series dataset from New York City including fare revenue and service availability.

Fare revenue and service availability are estimated from taximeter and odometer readings gathered during taxicabs inspections (each cab is inspected 3 times a year).

A total of 89,000 data points.

Hong Kong

The different variables are coupled together through a system of nonlinear simultaneous equations whose parameters are estimated from survey data.

Models are developed for: passenger waiting time, percentage of occupied taxis, vacant taxi headway, daily taxi passenger trips, and taxi waiting time.

Predicts level of taxi service and conduct sensitivity analysis for the introduction of any new taxi policies.

Forecasts are made for the years 86-95 to compare with observed data.

Forecasts are made for different policy variables.

The report include 1) definitions of the nature and scope of the PHV industry; 2) description of the salient characteristics of the industry; 3) provision of whatever information is available on these salient characteristics. There is a literature review mentioning among others a 1981 and 1986 national taxicab survey. Include some relevant statistics about the type of service provided and coverage.

The second part of the report present a more detailed case study of 8 urban areas.

National

Detailed case studies from: Ann Arbor, DuPage County, IL; Huston, Los Angeles, Montgomery County, MD., Portland, Seattle and the Wisconsin State.

The survey included 29,551 sedans, 2,788 mini-vans, 3,948 vans, 1078 mini buses, and 1,494 buses.

Examples from the case studies: Ann-Arbor average annual one-way trips: ADA Paratransit - 39,000 night rides, guaranteed ride home - 147,700.

Los Angeles - ADA paratransit: 1.7 million trips.

There are such data to all 8 case studies.

Data also includes passengers per service hour, average weekday ridership, and some financial data.

Charlottesville, VA.

An econometric model for number of trips as a count data model where the number of trips in month t for person I, is modeled as a Poisson random variable.

From FTA Section 15, included are data from 209 demand response systems located in cities of no more than 400,000 people.

Other data used in the analysis include: 1) the local paratransit provider administrative records providing information on each trip taken providing information of trips taken per month and characteristics of the individual taking trips; 2) Charlottesville Transit Service administrative records including information on each person applying for the paratransit service.

Florida

Simulation

For 1995, 431 vehicles were surveyed, making 1,764,588 trips (annual data), 1,589,077 vehicles hours, with average of 1.11 trips per vehicle hour, and 341 monthly trips per vehicle.

Some additional data based on the 29 agencies that responded to the questionnaire:

• Total monthly one-way trips (unreported by 5 agencies): 147,049
• Lowest monthly trip level reported: 36
• Highest monthly trip level reported: 765

The data are further break down by type of providers.

Data for 1999:

• Operating expense (Table 25): 1,420 million dollars
• Unlinked passenger trips (Table 26): 100 million
• Active passenger vehicles: 31,800

Data for 1999:

• Passenger miles (Table 30): 813 million
• Average unlinked trip length (Table 39): 8.1 miles
• Vehicles miles operated (Table 42): 718 million
• Vehicles hours (Table 43): 48 million
• Vehicle revenue miles (Table 45): 608 million
• Vehicle revenue hours (Table 45): 41 million

Average annual mile per vehicle vary by type of vehicle (sedans 54,194, vans 23,000-39,000, etc.

Average number of trips per week by vehicle type (sedans- 44, vans 4-19, Limo- 2-15, bus - 7.

Average distance per taxi trip 5.8 miles. Average passengers per trip 1.31.

Average CBD to airport trip 15.5 miles.

Location: The model is developed for Sacramento's Downtown/Natomas/Airport (DNA) Corridor.

Model: Rental car is one out of six airport access modes being modeled: Drive and park, rental car, get picked up/dropped off, taxi/shuttle van, public transit, and other courtesy van/dedicated hotel van. 1) Renting cost, 2) time to/from the terminal, 3) in-vehicle time, and 4) parking cost at destination are the attributes being modeled for the rental car mode.

This paper discusses the theory and application of the model, which is based on logistical chains. Livability and accessibility of urban areas are influenced by freight traffic resulting from logistical choices in the supply chain, like warehouse location, delivery frequencies, and vehicle type and routing. To support decision-making it is necessary to model these choices and their effects, in current and future situations.

GoodTrip is a tool to evaluate different concepts of freight distribution from both a societal as economical viewpoint, by using geographical, economical, and logistical data. Model output discriminates clearly between different alternative freight distribution concepts. The modeling results comply with empirical data and real life experience.

Overseas including a case study for the City of Groninger.

In GoodTrip the logistical chain links activities of consumers, supermarkets, hypermarkets, distribution centers and producers. Based on consumer demand, the GoodTrip model calculates the volume per goods type in meters cubed in every zone. The goods flows in the logistical chain are determined by the spatial distribution of activities and the market shares of each activity type - consumer, supermarket, hypermarket, distribution center, etc. This attraction constraint calculation starts with consumers and ends at the producers or at the city borders. A vehicle-loading algorithm then assigns the goods flows to vehicles. A shortest route algorithm assigns all tours of each transportation mode to the corresponding infrastructure networks. These results in logistical indicators, vehicle mileage, network loads, emissions and finally energy use of urban freight distribution.

Edited by: Michael A. Crew, Graduate School of Management, Rutgers University, Newark, NJ, USA Paul R. Kleindorfer

The Wharton School, University of Pennsylvania, Philadelphia, USA

Delays during transit time - loading goods into trucks from warehouses.

Valuation of time lost at transit is important to shippers and receivers which otherwise is not included in the vehicle operating costs and person travel times.

Paper-based on contextual stated preference methods and MNL models used to estimate the value of such factors from an Australian survey of freight shippers in 1998.

Importance of valuation of time spent in transit at warehouses.

Provides benefits in evaluating accurate freight travel times.

Provides information of airports across the world for the year 2001

Has information on almost all airports in the USA

Information on air cargo in terms of freight tonnage, size of airport warehouses, air cargo traffic

Number of carriers and proximity to other modes like rail, road, water

Size of warehouses at all airports in the USA (square feet)

Cargo space (acres, square feet)

Number of air service carriers

Traffic and tonnage (tons)

Number of aircraft movements

Availability of foreign trade zone, special services/facilities, U.S. Customs

Distance (in miles) to connecting transportation modes – rail terminal, ocean port, interstate highway, truck terminal

Freight tonnage at all airport warehouses will be useful to derive truck traffic to and from airports.

Size of warehouses also useful to estimate warehouse truck traffic.

Provide Ohio DOT with a clear picture of existing and future freight movements on Ohio's macro-highway corridors.

Assess the impact that future changes in the freight system and freight movement may have on Ohio's roadways.

Ohio statewide transportation plan - Access Ohio.

Development of a comprehensive, statewide, travel demand forecasting model, which includes a sophisticated freight-planning capabilities.

Truck flows in tons from, to and through the state of Ohio.

Truck flows in tons from and to warehouses in Ohio.

Distribution of truck flows and truck ton flows - inbound, outbound and internally in Ohio.

Origins and destinations of all truck trips in Ohio.

Freight tonnage at warehouses will be useful to derive truck traffic.

Number of truck trips by weight at warehouses.

Impact of telecom on transportation, including travel substitution, travel stimulation, and travel modification.

Survey and analysis of how telecom affects movement of people and goods.

United States Internet users and consumer goods purchased online from 1996 to 2002.

Percentage of total households who used the Internet for shopping.

Consumer goods purchased in millions ($$).

Forecasts of Internet users for 2001 and 2002.

Forecasts of consumer goods purchased online in 2001 and 2002 (in millions of dollars).

Report discusses more about the impacts of telecom on travel and movement of goods.

Discusses the advantages of telecommuting and its effect on transportation.

Only statistic relevant to our study is 'United States Internet users and consumer goods purchased online from 1996 to 2002.'

Two types of dot-com companies identified - digital and physical.

Digital dot-coms are Internet-based companies such as Yahoo, EBay and America Online.

Physical dot-coms sell physical products such as books, CDs, toys, etc., that are shipped to consumers.

Average revenue ($$) in 1998 and 1999.

Average number of employees and revenue per employee in 1998 and 1999.

Average gross income ($$) and gross profit margin (%) in 1998 and 1999.

Comparative statistics of above mentioned variables across publicly traded digital and physical dot-coms.

Forecasts of Internet users for 2001 and 2002

Forecasts of consumer goods purchased online in 2001 and 2002 (in millions of dollars)

E-commerce overview

B2B

B2C

Impact of e-commerce by industry type

B2C e-Tailing revenue in 1999 ($$)

Number of shipping packages in 2000

B2B revenue statistics - 1998 to 2004

B2B commerce conducted over the Internet by industry category

B2C revenue in 2004 ($$)

Residential package delivery in 2003

Quarterly reports on United States retail e commerce sales in billions of dollars

Monthly Retail Trade Survey (MRTS)

United States Manufacturing - Total and E commerce value of shipments ($$) for 1999 and 2000

United States Merchant Wholesale Trade - Total and E commerce sales ($$) for 1999 and 2000

United States Merchant Wholesale Trade - Total, E commerce and EDI (electronic data interchange) Sales ($$) for 2000

United States Selected Service Industries - Total, E commerce Revenue ($$) for 1999 and 2000

United States Retail Trade - Total and E commerce Sales ($$) for 1999 and 2000

United States Electronic Shopping and Mail-order Houses - Total and E commerce Sales by Merchandise Line for 1999 and 2000

2000 Annual Survey of Manufactures

2000 Annual Trade Survey

2000 Service Annual Survey

2000 Annual Retail Survey

Value ($$) of construction contracts awarded by State and Facility (airport, bridges and tunnels, docks, piers and wharves, highways and railways).

Number of contracts awarded by Facility.

Number of contracts awarded by State.

Transportation construction contractor employment by facility type.

To provide environmental and regulatory background necessary to analyze traffic issues

To evaluate potential traffic impacts associated with project construction

Average daily workforce.

Average daily vehicle trips - construction workers and heavy trucks.

Daily peak-hour vehicle trips - transporting workforce and equipment and material deliveries.

Washington State DOT and U.S. DOT, FHWA Research Project T1803, Task 37

http://199.79.179.82/sundev/detail.cfm? ANNUMBER=00921453&STARTROW=1&CFID=87145&CFTOKEN=15535947

In 1998, the Washington State Service Patrol Study Steering Committee provided additional towing services to improve incident removal from the most congested sections of Puget Sound area freeways. The primary goal of a Service Patrol is to provide quick response to incidents and clear roadways as rapidly as possible in high-volume areas during peak traffic volumes.

The committee also recommended an evaluation of the system to determine and compare the effectiveness of the service modes, specifically, the Washington State Patrol, contracted tow operators, WSDOT tow trucks operated on the floating bridges, and privately sponsored motor assistance vehicles such as that of the AAA.

The purpose of this report is to examine different methods of service delivery and to provide lessons learned for future implementation.

During the six months study period, 5,000 Service Patrol assists or contacts occurred on the patrolled segments on I 5, I 405, SR 16, floating bridge on I 90 and SR 520. The types of incident data included are:

1. Number of motorist contacted;
2. Distribution by time;
3. Distribution by location;
4. Method of detection and notification;
5. Frequency of false alarms;
6. Type of incidents;
7. Lane blockage; and
8. Response time.

UC, BERKELEY, INSTITUTE FOR TRANSPORTATION STUDIES 15071957

http://199.79.179.82/sundev/detail.cfm?ANNUMBER=00419626&STARTROW=11&CFID=87145&CFTOKEN=15535947

Illinois

Regression equation was developed which estimates annual hours spent policing as follows:

• ti = 51.8 + 4.257V - 0.0002V2 (for Controlled Access Highway)
• ts = 11.7 + 5.2127V - 0.00172V2 (for Primary Highway)
• tr = 294.6 + 9.483R + 2235.0A (for Rural Highway)

Where:

• t = Time in hours annually spent policing
• V = Volume in vehicle miles (000's)
• R = Rural population (000's)
• A = ADT (000's)

This paper estimates time in hours annually spent policing and VMT for policing. This regression equation might be useful for our study.

This analysis showed very strong relationships between historic workload and independent variables such as volume of traffic and rural population. Workload is defined as the number of hours spent annually on police services. Included in the workload is traffic enforcement, motorist assistance, removal of abandoned vehicles, and traffic control. Volume is measured by vehicle miles.

Sears' Product Repair Service and Sears Logistics Service department hired outside consultant AMEC to form a project team with its internal resources for the redesign of the carry-in repair network.

Network optimization program SAILS (Strategic Analysis of Integrated Logistics Systems) was applied for this work. SAILS uses a mixed integer linear program approach, specifically designed to solve large-scale multi-echelon network optimization problems. The echelons are: Repair facilities, Repair facilities warehouses, Cross-Dock facilities, and Access points.

GeoRoute is an arc routing Software package that includes a route optimization module based on the GENIUS traveling salesman problem heuristic. The territory under study is represented by a directed graph in which arcs correspond to streets. The system also introduces artificial arcs to account for street crossings, street changes, and turns.

This report describes experiments performed using GeoRoute to determine optimal routes for garbage collection and for snow plowing operations on 30 street networks. The experiments tested various values of penalties in order to arrive at conventional packages of penalties for use in various situations. The results help GeoRoute users select sets of penalties without having to ponder a large set of possible parameters.

Montreal, Canada

GeoRoute is a routing software and USPS and many other companies use this package for their traveling salesman optimization. The author applied the software and uses different costs/penalties to optimize a 30 street network. As a case study he determined optimum routes for GARBAGE COLLECTION and SNOW PLOWING OPERATIONS.

The purpose of this paper is to provide an overview of the development of a commercial vehicles travel model set up for ARC, the Metropolitan Planning Organization (MPO) for the Atlantic Region, and the Georgia Tech emissions model.

The development of the Atlanta commercial vehicle and truck model set was divided into three basic phases. The data collection phase which identifies data elements and formulation of a survey instrument. The data collection phase which identifies data elements and formulation of a survey instrument. The second phase consisted of the development of the truck trip generation and the trip distribution models. The final phase is the integrated heavy-duty vehicle emissions module.

Trip purpose coded in the NTPS survey are:

• Work
• Work-related
• Return to work
• Family and Personal Business
  • Shopping
  • Medical or Dental
  • Take someone somewhere
  • Pick up someone
  • Other family and personal business
• School
• Religious activity
• Social and Recreational
  • Vacation
  • Visit friends and relatives
  • Out to eat
  • Other social and recreational
• Return home
• Other

Trip Modes Coded:

Automobile, Van, Sport utility vehicle, Pickup truck, Other truck, RV (recreational vehicle), Motorcycle, Other POV, Bus, Amtrak, Commuter train, Streetcar/trolley, Subway/elevated rail, Airplane, Taxicab, Bicycle, Walk, School bus, Other non POV, Legitimate skip, Not ascertained.