Investigating heterogeneity in travel behaviour change when implementing soft transport interventions: A latent class choice model

Abstract

Attracting more travellers to shift towards green modes plays a significant role in sustainable transportation development. Soft transport interventions are important strategies for facilitating voluntary travel behaviour change. This study investigates the effects of two soft transport interventions, information intervention and public transport service improvement, on heterogeneous traveller groups’ behaviour change. Beijing, China was selected as the case study site. Firstly, three heterogenous traveller groups were identified based on latent class cluster analysis: Group A (20.4%, travel in low‐frequency and prefer multimode), Group B (30.3%, travel in middle‐frequency and prefer the car) and Group C (49.3%, travel in high frequency and prefer green modes). Then, latent class choice models under two transport interventions were established respectively. The findings indicate that information intervention decreases Group B's preference for car use and exclusion of bicycles. Little effect is observed on travel behaviour change of Group A and C. Public transport service improvement significantly reduces car use of Group B and C. This study demonstrates a limited influence of information intervention on travel behaviour change and public transport service improvement is effective in achieving car use reduction. The results provide decision makers with the information needed to target interventions for different traveller groups.

A New Combined Model for Assessing the Perceived Accessibility of Public Transport by Consumers in the Megapolis

“…Representativeness was ensured using quotas by sex and age in accordance with the structure of the population of Moscow, which was 12 million and 678 residents officially [63]. To build a sample for online survey, quotas were developed for the frequency of public transport use (as it was recommended in the study [12]). This decision was dictated by the results of our previous study that was conducted at the beginning of 2020 before the introduction of restrictions on movement due to the coronavirus infection and showed that more than 50% of respondents did not indicate public transport among the main ways of moving around Moscow (February -March 2020, a sample of 2275 respondents, geography of the study -Moscow within the boundaries of 2012).…”

“…Its solution is critical to the sustainable development of urban public transport in urban agglomerations. In a study by scholars from Beijing, China, three heterogeneous latent passenger groups were identified: group A (20.4%, travel with low frequency and prefer multimode transport), group B (30.3%, travel with medium frequency and prefer a car), and group C (49.3%, travel with high frequency and prefer green modes of transport) [12].…”

Urban Day-to-Day Travel and Its Development in an Information Environment: A Review

“…Improve urban transportation conditions [76][77][78] Willing to accept information prompts [79-81] Provide travel decision-making assistance [35,[82][83][84][85] Willing to face risk with the help of information [86] Reduce emissions, protect environment [83] Information provided by mobile devices affects most [87] Negative Bad effects of improper information dissemination [88] Unwilling to accept information prompts [89][90][91][92][93][94] Real road network information may not lead to best traffic distribution [95] Information prompts do not lead to traffic condition improvement [96] A majority of travelers are indifferent to information prompts [97-99] Better to follow intuition than follow information prompts [100] Neutral Interaction in social platforms is also a part of travel information [101,102] Different attitudes towards information prompts [103][104][105][106] Information prompts may not always have a fixed effect [107,108] Effectiveness varies depending on the penetration rate of ATIS [109,110] Information dissemination has different strategies [111] Differentiated information dissemination considering different personalities of receivers [112] Correct and incorrect information may both have good effects [113] Different impact of information prompts inside/outside congestion area [114] 3.…”

“…Their conclusion is that in many cases, providing real road network information by ATIS cannot achieve the optimal macro traffic flow distribution [95]. In the process of improving public transportation service levels through soft transportation interventions such as traffic information prompts, Fan et al have found that effective prompts do not necessarily imply a significant improvement in public transportation service levels in the perception of travelers [96]. Han et al have pointed out that day-to-day travelers do not pay as much attention to whether the road network has reached its optimal state as urban traffic governing officials, and often exhibit a selfish characteristic.…”