Transportation demand management policies have the potential to significantly alter individuals’ routine travel behavior. One of the key responses of private car passengers to the implementation of congestion plans is the adjustment of trip departure times. If the management of changing trip departure time shifts is not effective, the emergence of traffic peak periods before and after the plan may exceed current peak traffic levels. A literature review reveals that the investigation of trip departure time adjustments has received limited attention, and behavior regulation strategies to mitigate peak period formation have not been explored. The primary aim of this paper is to develop scenarios integrating transportation demand management strategies to prevent the occurrence of a tipping point. To achieve this, the effects of social and economic factors, travel characteristics, and citizens’ attitudes toward transportation demand management policies on private car passengers’ departure time shifts in the congestion zone have been examined. To estimate the probability of departure time adjustments, 2,256 individuals were interviewed in Shiraz, yielding 13,536 observations through Stated-Preference (SP) analysis. The calibration of the binary logit model has demonstrated that congestion pricing policies, parking fees, reductions in public transportation travel time, and enhancements in bus service quality exert significant influence on departure time modifications. Based on extensive policy considerations, 27 out of the 36 defined scenarios—those generating a peak period outside the congestion plan’s implementation timeframe—have been deemed unsuitable for execution. This paper introduces a novel probability-thresholding framework that operationalizes behavioral model outputs to proactively screen Transport Demand Management (TDM) scenarios for secondary congestion risks — a methodological advancement not previously applied in developing-city contexts.
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