Deriving transport appraisal values from emerging revealed preference data

Transport demand models are widely used to inform policy making and produce forecasts of future demand. A core output derived from is the Value Travel Time (VTT), which provides insights on trade-offs that travellers willing make in terms travel time cost. VTT estimates a critical input cost-benefit analyses feasibility assessments potential projects thus play crucial role transport planning decisions. While much early work made use revealed preference (RP) data, their decreased due growing concerns about reporting errors may result omitted observations measurement model inputs. As consequence, measures have, for last two decades, primarily been estimated using state-preference (SP) surveys. SP methods can assess individual controlled manner, they prone behavioural incongruence. More recently, RP data passively-collected sources have raised promise accounting some limitations traditional surveys minimal (or even no) active respondent. The present study utilises such dataset combined 2-week trip diary captured through smartphone GPS tracking with household survey containing socio-demographic information. mixed Logit mode choice was specified parameters were then applied National Survey calculate estimates. Those further adjusted based distances get more representative national values. This process resulted similar official UK guidelines appraisal obtained where our results not affected by response quality or artefacts. findings hence strengthen case shifting towards passively generated important practitioners.