Liang Long, PhD candidate, Advisor: Dr. Lin

Transferability of Household Travel Survey Data in small areas using the National Household Travel Survey data and the census data

Household travel surveys play a key but expensive role in transportation planning, with about $200 per household or half million dollars for just a 2,000 household sample. My research objective is to find an alternative solution for scant sample data issues especially in small areas and subpopulation groups (e.g., Hispanic, urban low-income group). The general idea is to borrow data from auxiliary sources such as-national household travel surveys (NHTS) and Census Transportation Planning Package (CTPP) for local areas.

In the study, the statistical clustering approach coupled with GIS spatial analysis is firstly applied to characterize neighborhood lifestyles using sixty-four features extracted from the Census Transportation Planning Package (CTPP) 2000 data. The resulting ten clusters reveal residential location preference as a result of individual or household socio-economic status such as income, occupation, age and ethnics. Travel characteristics of each cluster using the 2001 National Household Travel Survey (NHTS) travel data suggest five factors influencing household travel, socio-economic status, residential location and land use, household life cycle, activity type, and ethnics. Each neighborhood type is distinctively defined and reasonably homogenous in terms of socio-economic and travel characteristics.

Then the feasibility to transfer travel characteristics, i.e. trip rate, mode share, vehicle miles traveled etc. across geographic areas is tested by proposing hierarchical-random effect models. Although there have been similar studies, this is the first to test transferability at the disaggregated level by associating household level and neighborhood level characteristics with travel behavior, rather than the simple, conventional approach of comparing means. Equally important, it has practical values particularly to small metropolitan areas.

In the last step, small area estimation methods are applied to generate totals or means of travel attributes for target areas by using NHTS and CTPP. Three different methods are examined: Generalized regression estimator, synthetic estimator and empirical linear unbiased predictor.

Wenjing Pu, PhD student, Advisor: Dr. Lin

Travel Time Prediction on Signalized Urban Streets Using AVL Buses as Probes

Providing travelers travel time and related information is a crucial part of the advanced traveler information system (ATIS). Most metropolitan areas in the U.S., however, do not provide travel time information for urban streets because they are suffering from data unavailability. Utilizing automatic vehicle location (AVL) equipped buses as probe vehicles could be a cost-effective approach to advance ATIS on urban streets. The objective of this research is to develop prediction algorithms for urban street travel time in City of Chicago by utilizing the existing AVL buses as probes so that additional costs invested in traffic monitoring (e.g., loop detectors) on urban streets can be avoided.

The major challenges are 1) characterizing the relationship between bus travel time and general traffic stream travel time; and 2) predicting bus arrival time and general vehicle travel time. These problems become even more challenging under congested traffic conditions (both reccurring and non-reccurring). Sufficient network coverage and sample size in term of number of bus probes per link are also important issues in determining the reliability of travel time information.

The achievements of this research will benefit both public and private transportation users, increase bus ridership and reduce vehicles waiting time and emissions.

Peng Wang, MS Candidate, Advisor: Dr. Lin

Bus Route Schedule Adherence Assessment Using Automatic Vehicle Location (AVL) Data (MS THESIS)

The thesis demonstrates an optimization method to develop a composite performance index of bus route schedule adherence by combining two elementary metrics: running time adherence and headway regularity together. The optimization method is build based on the data envelopment analysis (DEA) model and it incorporates the use of historical AVL data, which are used to generate the afore-mentioned elementary metrics. The method is applied to assess the service reliability performance of 48 bus route-directions selected from the bus network of Chicago Transit Authority (CTA) and the obtained performance scores are discussed and analyzed.

A research paper is being written with an emphasis of conveying the idea of performance index development using AVL data and DEA methodology, as well as exploring the practical implications.

Improving the CTA Capital Improvement Program Management (CIPM) Asset Management (AM) System Prototype

Peng is also working as a graduate research assistant at the Urban Transportation Center (UTC) of College of Urban Planning and Public Affairs (CUPPA). At UTC, his current project is making improvements to the CTA CIPM AM System Prototype, which was initially developed by Dong Zhai, a former MS student in CME in 2003. The CTA CIPM AM system is a tool to facilitate and enhance CTA CIPM’s data management capacities. It links with CTA’s asset management database and Geographic Information System (GIS) software. Users can use the system to query data, generate geographic presentations, and conduct spatial analysis.

Minyan Ruan, PhD student, Advisor: Dr. Lin

Modeling Land Use, Bus Ridership and Air Quality: A Case Study of Chicago Bus Service

Transit ridership varies at the route, route segment and bus stop level. Previous study on the route level has shown that higher population in non-Hispanic city poor for unit route length will decrease the bus ridership, while longer road length in urban elite for unit route length will increase the bus ridership. The number of stops within the route, which represents the transit service, has the potential to increase the total bus ridership in that route. Also, ridership displays a seasonal trend in the model, with significantly lower passengers in holiday seasons like December and August.

However, the assumption of the route level model is that the land use and demographic characteristics are homogenous, which is apparently not right in most cases. I am now working on modeling the bus ridership and land use in Chicago area in stop level, trying to find out the impact of land use on CTA bus stops. The automatic passenger counter (APC) is now installed in a number of the CTA bus, which covers most of the routes and thus provides a good source of ridership data in the stop level. The variation of ridership across stops depends on the stop-specific variables within the stop service area, which will include pedestrian roadway environment, stop amenities, sociodemographics in land use types. Besides, the transfer passengers from automobile, bicycle, train and other bus stops, will be taken into account by adding variables like the auto parking availability, biking availability, train accessibility and the other bus stop accessibility. Adjacent routes can be complementary routes that will increase the ridership, or competing routes that will increase or reduce the ridership.

A mixed effect model with both spatial and temporal random effect will be built, considering the autocorrelations between stops within the same route and between different time periods within the same route.

Taha Hossein Rashidi, PhD Candidate, Advisor: Dr. Mohammadian

IDOT: Improving the Transit System for Seniors

As a research assistant at the University of Illinois at Chicago, I am currently working on a project funded by Illinois Department of Transportation (IDOT) with the goal to improve the effectiveness of transit systems for seniors. We have conducted an opinion survey of seniors attempting to understand their transit use behavior. Given the aging population, this is an important problem to address (by 2030 number of seniors in the US will be doubled). The percentage of elderly transit users in Northeastern Illinois is low in comparison to the number of elderly who could utilize transit services. A survey of seniors in Chicago region was designed and conducted to collect the information required to analyze the effectiveness of strategies that have been implemented, or are being planned, to attract this age group. The main goal of the study that I am working on is to enhance transit providers’ awareness on how to be more effective in attracting senior riders. For this purpose I have designed a questionnaire which consists of four main sections dealing with different trip purposes (Shopping, Doctor Visit, Social and Recreational, and Work trips) and different travel modes in Chicago region including three commonly used public transit modes of Metra, CTA and PACE, as well as a section asking general questions on respondents’ socio-economics, location, and built environment attributes. Survey respondents are also asked to provide their opinions about transit services within the region.

A stated preference analysis is then conducted using this information. The results of the analysis represent seniors’ preferred alternatives and effective strategies for system improvement. Discrete Choice models are used to estimate the probability of utilizing each mode for this age group. Furthermore, policy analysis using the modeling results introduces the effective factors that should be considered and applied to improve transit services that could encourage senior citizens to use public transportation facilities more often.

Yong-Ping Zhang, PhD Candidate, Advisor: Dr. Mohammadian

Developing and Validating a Household Travel Data Transferability Model

Traditionally, Metropolitan Planning Organizations are required to have their models calibrated on a continuing basis using new data. However, new survey data required to support these models do not exist in most urban areas. This makes it very difficult to calibrate existing models or develop new travel demand models using emerging modeling techniques. As a result, the need to assess potential approaches and develop knowledge on how to transfer information collected in one context and use it in another context is becoming critical.

The ultimate goal of my research is to develop and validate a household travel data transferability model that can facilitate the use of national household travel survey data to a local area. Furthermore, using the transferred estimates and synthesized population, the study will attempt to micro-simulate disaggregate household travel survey data. This can reduce or eliminate the need for a large data collection in the application context and will allow small MPOs to utilize transferred travel attributes from similar areas.

Additionally, in larger metropolitan areas where MPOs can afford carrying out a standard household travel survey, a small-scale survey can be conducted periodically for improving the quality of the simulated data by updating the parameters of the transferred data. Such a small sample can reduce the need for a regular full-scale survey and increase the time interval of the full-scale survey to every 6-7 years or even longer.

Joshua Auld, PhD Candidate, Advisor: Dr. Mohammadian

Development of an Improved Scheduling Model for use in Micro-simulation Modeling

For my research I am currently focusing on developing an improved activity scheduling model for use in regional microsimulation modeling. The activity scheduling model will eventually be used with other data sources to develop an activity-based travel demand model for the Chicago area. These models have the potential for greatly increasing the accuracy of future travel demand estimates. More accurate models would have a large impact on the planning and policy decisions for which these models are usually used.

At the current stage, I am using data mining techniques to identify patterns in individuals’ activity rescheduling behavior. This is done using activity scheduling process survey data, which includes information on how and when people schedule activities, as well as how they reschedule the activities when conflicts in the schedule arise. The patterns observed in the activity scheduling and rescheduling data will than be used to develop rules for how activity conflicts are resolved by different individuals. The use of rule-based conflict resolution will potentially improve the accuracy of activity scheduling models. In the future, the conflict resolution model will be combined with an activity generation model to create an activity scheduling simulator. This will then be used with activity survey data from several areas to initially check the transferability of the rescheduling rules, by comparing the simulated activity schedules against the actual schedules obtained from the survey data.

-- Main.sareva2 - 26 Aug 2010

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