Influence of pricing on mode choice decision in Jakarta: A random regret minimization model. Issue 1 (March 2019)

• Record Type: Journal Article
• Authors: Belgiawan, Prawira F.; Ilahi, Anugrah; Axhausen, Kay W.
• Is Part Of: Case studies on transport policy. Volume 7:Issue 1(2019)
• Page Start: 87
• Page End: 95
• Publication Date: 2019-03
• Subjects: Electronic road pricing -- Random regret minimization -- Value of travel time savings -- Elasticities; Transportation and state -- Case studies -- Periodicals; Transportation -- Planning -- Case studies -- Periodicals; Transportation -- Research -- Case studies -- Periodicals; 388.05
• DOI: 10.1016/j.cstp.2018.12.002
• Languages: English
• ISSNs: 2213-624X

Abstract

Highlights: The Government of Jakarta is planning to implement electronic road pricing (ERP). The ERP is for commuters who pass through CBD, we called it contribution cost (CC). There is an alternative modelling approach called random regret minimization (RRM). We compare two RRM modelling approach, model 1 with CC and model 2 without CC. The comparisons are for model fit, value of travel time savings and elasticities. Abstract: To reduce traffic congestion, the Government of Jakarta is planning to implement electronic road pricing (ERP) for commuters who pass through arterial roads in Jakarta's CBD area. This paper studies the implementation of ERP, which in this study is called contribution cost, by utilizing the recently introduced alternative choice model approach called random regret minimization (RRM). In RRM when deciding, individuals are assumed to minimize their anticipated regret as opposed to maximizing their utility. A stated preference (SP) survey has been conducted with 507 respondents. Four alternative modes (public transport, park and ride, car, and motorcycle) were presented with several attributes such as travel time, travel cost, waiting time, transfer, parking cost, and contribution cost. The SP survey was divided into two parts, part 1 used for Model 1 with all attributes including contribution cost, and part 2 used for Model 2 with only travel time and travel cost (without the contribution costs). In total 6003 observations for 12 scenarios in Model 1, and 4011 observations for 8 scenarios in Model 2 were obtained. Comparing model fit and prediction accuracy, Model 2 outperforms Model 1. Regarding the value of travel time savings (VTTS), it appears that the incorporation of contribution cost (and other attributes) results in substantially higher VTTS for Model 1 compared to Model 2. Finally demand elasticities are larger than one for public transport, park and ride and car travel time. The results also show that car contribution cost elasticity is substantially higher than motorcycle contribution cost elasticity.