Algorithm for categorization of traffic congestion status into different classes
Step 1: construction of a weighted set of congestion containing weights for different routes based on the congestion weight table.
Step 3: performing union operation between the congestion and average speed weighted sets using the formula
In similar manner, the mean congestion value was determined for each route based on which the range in the mean congestion index values was determined for the study area.
The status of congestion and average speed in different routes are presented in Table 1 and 2 respectively.
This situation results practically because of some serious limitations of CIV in deciphering the status of congestion such as first, the CIV is designed to evaluate only a single status of congestion in that the routes with CIV greater than 2 are categorized as congested whereas the congestion status of the routes with CIV smaller than 2 remain undefined.
Third, routes with CIV close to 2 could be associated with same or nearly same traffic conditions in the field but the crisp characteristic of CIV equation fails to assign any congestion status to such routes.
Traffic congestion is a challenge not only to urban planners but also to the environmentalists as it can be used as an indicative parameter for air and noise pollution.
Therefore, in the present investigation, the status of traffic congestion was categorized into different classes using congestion index value and average speed to assess the status of traffic congestion in different parts of the city (Table 3).