Abstract | Full Paper | PDF | Printer friendly format

Abstract
The paper proposes using multi-temporal spatial datasets to improve design and efficiency of traffic monitoring programs. Travel patterns are a result of demand for mobility by the users of the transportation system. These users have a specific trip pattern, which is based in part on their socio-economic level and their work habits. Naturally, the roadways, which serve a region, are instrumental in its development and influence the trips generated. In turn, the number, mode and the daily/weekly/monthly/yearly spread of trips are chiefly determined by landuse. This close inter-relationship between landuse and the transportation system is necessary to be understood and modeled for appropriate analysis.

The Federal Highway Administration (FHWA) mandates the states to collect traffic count information at a specific interval to meet the parameters of the Highway Performance Monitoring System (HPMS). The state of Iowa has approximately 130 permanent count locations spread across the state for traffic data collection and a manual count schedule to satisfy the federal mandates. For primary (principal) roads, a data collection cycle is completed once every four years (one quarter of the state is counted every year). Secondary (non-principal) roads are counted only every eight years. Growth and changes in landuse as well as infrastructure development affect traffic patterns, and clearly, some areas grow more than others. To respond to this, and to make the procedure more efficient and provide the timeliest data, the present process allows out-of turn counting. The determination of change and prioritization is based on recommendations from state, county, and city officials. However, this procedure is highly subjective and only “significant” changes in landuse and network are considered. The FHWA Traffic Monitoring Guide recommends that state highway agencies examine existing traffic volume information and optimize data collection frequency and coverage. Therefore, identifying regions with significant landuse change would certainly be useful in prioritizing the updating procedure of Iowa DOT databases.

Change detection is a process by which two datasets describing the same region can be used to identify sub-regions, which have appreciable change. Extensive research has been performed to define the impact of landuse changes on the transportation sector.

Commercially available software have inbuilt procedures for change detection analysis and generate the statistics to quantify the type and level of landuse change. These procedures use attributes such as texture, tone, color and pattern in the imagery and translate into landuse types.

We know that changes in land use adjacent to highway corridors can have a high correlation with changes in traffic volume and traffic distribution patterns. By including these data in the overall process of traffic count management, the quality and timeliness of traffic count data could potentially be increased and the efficiency of the current process enhanced.

The suggested approach is to collect the latest statewide coarse resolution imagery along with historical datasets, to identify regions with significant changes. Then, high-resolution data could be acquired over the regions identified from the coarse resolution imagery for further analysis. The classification system used is based on the National Land Cover Data (NLCD 2000) that is a component of USGS land cover characterization program. The type, direction and level of change detection is identified from multi-temporal imagery and correlated with changes in the traffic counts.

Results could enable redirecting traffic count activities, and related data management resources, to areas that are experiencing the greatest changes in land use and related traffic volume. Conversely, areas where traffic counts are static or changes are statistically insignificant over time would be counted less frequently. Other less costly methods might be employed to generate the data needed for these locations.