Abstract
Time-of-day modelling is an additional step to the conventional four-step Travel Demand Models (TDMs). Here, the target is to obtain more detailed outputs over the temporal dimension. With this additional step, daily (24-hour) travel demand is distributed into a discrete number of time-windows.This paper aims to identify the most precise time windows that maximise the trips that fall within a given time-window and minimise the trip-tailing associated with it. The trips-in-motion method follows a more logical approach to capturing the entire trip duration. The Colombo Metropolitan Region Transport Masterplan database, developed in 2013, is analysed using Bentley Cube Voyager transport demand modelling software. The most precise starting timestamps of two-hour time windows were selected for the morning, mid-day, and evening peaks at 6:30 AM, 01:30 PM and 05:00 PM.This study has developed a systematic approach to identify time-windows as input for time-of-day based modelling. This attempt is an initial step to simulate the third-dimension of a trip, which is called the temporal dimension of TDMs.Finally, it is recommended to study the shift in peak periods with the change in time of demand, which would be the behavioural change most expected to occur post- COVID-19.
Highlights
A trip is a movement between two geographic points of the spatial dimension and a movement between two timestamps of the temporal dimension
The analysis reveals that the W120 starts at 6:30 AM has the highest value (27% of daily door-to-door trips) for the trips only moving within the W120
Deriving time windows using time matrix is suggested as an initial step to time-of-day model development
Summary
A trip is a movement between two geographic points of the spatial dimension and a movement between two timestamps of the temporal dimension. Transport systems connect many such spatial points and facilitate trips to move forward along the temporal dimension. Traffic observed in the road transport network is an aggregation of many such trips that are moving within a particular time-window. Time-specific demand estimation is a significant concern in metropolitan Travel Demand Models (TDMs) [1]. The temporal resolution of static TDMs is usually into a few discrete time-periods [2]. The dynamic TDMs simulate shorter time intervals, typically 15 minutes each [3]
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