Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Kumar, Ajay"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Item
    Chained Trips in Montgomery County, Maryland
    (Institute of Transportation Engineers, 1995-05) Kumar, Ajay; Levinson, David M
    This paper analyzes the 1987-88 Metropolitan Washington Council of Governments home interview survey to understand how work trips are combined into trip chains and to relate trip chaining with demographic and travel characteristics. The focus is on the work trips during the morning and afternoon peak period and the stops made on the way for performing nonwork activities. The work trips during the afternoon period are much more likely to involve trip chaining as compared to the morning period. Women are more likely to link work trips with other activities as compared to men. Stops are closer to home than work.
  • Thumbnail Image
    Item
    Density and the Journey to Work
    (Center for Business and Economic Research, University of Kentucky. Published by Blackwell Publishers, 1997) Levinson, David M; Kumar, Ajay
    This paper evaluates the influence of residential density on commuting behavior across U.S. cities while controlling for available opportunities, the technology of transportation infrastructure, and individual socio-economic and demographic characteristics. The measures of metropolitan and local density are addressed separately. We suggest that metropolitan residential density serves principally as a surrogate for city size. We argue that markets react to high interaction costs found in large cities by raising density rather than density being a cause of those high costs. Local residential density measures relative location (accessibility) within the metropolitan region as well as indexing the level of congestion. We conduct regressions to predict commuting time, speed, and distance by mode of travel on a cross-section of individuals nationally and city by city. The results indicate that residential density in the area around the tripmaker's home is an important factor: the higher the density the lower the speed and the shorter the distance. However, density's effect on travel time is ambiguous, speed and distance are off-setting effects on time. The paper suggests a threshold density at which the decrease in distance is overtaken by the congestion effects, resulting in a residential density between 7,500 and 10,000 persons per square mile (neither the highest nor lowest) with the shortest duration auto commutes.
  • Thumbnail Image
    Item
    A Multi-modal Trip Distribution Model
    (Transportation Research Board, 1995) Levinson, David M; Kumar, Ajay
    This paper presents a multimodal trip distribution function estimated and validated for the metropolitan Washington region. In addition, a methodology for measuring accessibility, which is used as a measure of effectiveness for networks, using the impedance curves in the distribution model is described. This methodology is applied at the strategic planning level to alternative HOV alignments to select alignments for further study and Right-of-Way preservation.
  • Thumbnail Image
    Item
    Operational Evidence of Changing Travel Patterns
    (Institute of Transportation Engineers, 1994) Levinson, David M; Kumar, Ajay
    This paper utilizes a traffic counts database covering a ten year period (1976-1985) to identify travel trends for Montgomery County, a suburb of Washington D.C. Generally, travel behavior is analyzed using person based travel survey data. The use of traffic counts to understand travel behavior is a relatively new approach. Unlike household surveys, which are typically characterized by respondent and sample bias, and require special effort for their collection, traffic counts are routinely collected by Departments of Transportation and provide the best available measure of observed traffic volumes. The study provides fresh evidence to support some of the earlier findings: an increase in lateral commuting as a share of travel, changes in work and non-work trip proportions, and increase in peak spreading. An interesting result in this paper relates to a more pronounced directionality in radial as compared with lateral trips. The relative symmetry of traffic flows along lateral routes compared with radial routes results in better utilization of the suburban road network. Non-work trips emerge as the more elastic trips, shifting to off-peak hours with an increase in congestion.
  • Thumbnail Image
    Item
    The Rational Locator: Why Travel Times Have Remained Stable
    (American Planning Association, 1994) Levinson, David M; Kumar, Ajay
    This paper evaluates household travel surveys for the Washington metropolitan region conducted in 1968 and 1988, and shows that commuting times remain stable or decline over the twenty year period despite an increase in average travel distance, after controlling for trip purpose and mode of travel. The average automobile work-to-home time of 32.5 minutes in both 1968 and 1988 is, moreover, very consistent with a 1957 survey showing an average time of 33.5 minutes in metropolitan Washington. Average trip speeds increased by more than 20 percent, countering the effect of increased travel distance. This change was observed during a period of rapid suburban growth in the region. With the changing distributional composition of trip origins and destinations, overall travel times have remained relatively constant. The hypothesis that jobs and housing mutually co-locate to optimize travel times is lent further support by these data.
  • Thumbnail Image
    Item
    Specifying, Estimating and Validating a New Trip Generation Model: Case Study in Montgomery County, Maryland
    (Transportation Research Board, 1994) Kumar, Ajay; Levinson, David M
    This paper discusses the development of an afternoon peak period trip generation model for both work and non-work trips. Three data sources are used in model development, a Household Travel Survey, a Census-Update Survey, and a Trip Generation Study. Seven one-direction trip purposes are defined, specifically accounting for stops made on the return trip from work to home. Trips are classified by origin and destination activities rather than by production and attraction, so reframing the conventional schema of home-based and non-home-based trips. Prior to estimating the model, the Household Travel Survey was demographically calibrated against the Census-Update to minimize demographic bias. A model of home-end trip generation is estimated using the Household Travel Survey as a cross-classification of the demographic factors of age and household size in addition to dwelling type. Non-home-end generation uses employment by type and population. The model was validated by comparison with a site based Trip Generation Study, which revealed an under-reporting of the relatively short and less regular shopping trips. Normalization procedures are developed to ensure that all ends of a chained trip were properly accounted for.
  • Thumbnail Image
    Item
    Temporal Variations on the Allocation of Time
    (Transportation Research Board, 1995) Levinson, David M; Kumar, Ajay
    This study investigates the allocation of time and trip-making across time-of-day, day-of-week, and month-of-year, as well as over the past forty years. Some interesting findings result. People are working much more, shopping somewhat more on weekends, and stay at home less today than forty years ago. Time spent in travel on each weekend day (Saturday or Sunday) exceeds that on any weekday, as it did forty years ago. Time spent shopping on a typical day in the busiest month (December) is more than double that in the least busy month (September). Monthly variations in daily time in travel exceed 10 percent. The time of day patterns of shop and other trips for workers and nonworkers are both rational: nonworkers peak in mid-day away from rush hour while workers peak just after work, indicating trip chaining.