Examining impacts of time-based pricing strategies in public transportation: A study of Singapore

Adnan, Muhammad; Biran, Bat-hen Nahmias; Baburajan, Vishnu; Basak, Kakali; Ben‐Akiva, Moshe

doi:10.1016/j.tra.2020.08.010

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Furthermore, how the waiting time affects mode choice is also worthy of investigation (Sun and Xu, 2012). Finally, an interesting and important research direction is to develop time-and station-dependent transit fare schemes to flatten peak hour demand and thus reduce the mismatch between demand and supply (Yang and Tang, 2018;Lu et al, 2020;Li et al, 2018;Adnan et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…For example, providing reduced-rate fares to off-peak trips can flatten the peakhour demand (e.g., shift peak-hour trips to pre-peak and after-peak hours). The temporally differentiated fare scheme has been studied in many research (Yang and Tang, 2018;Lu et al, 2020;Li et al, 2018;Adnan et al, 2020). A few real-world practices show that properly designed off-peak discounts can help reduce metro crowding (Halvorsen et al, 2016;Greene-Roesel et al, 2018).…”

Section: Potential Solutions For Out-of-station Queueingmentioning

confidence: 99%

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Quantifying out-of-station waiting time in oversaturated urban metro systems

Zhu¹,

Cheng²,

Wu³

et al. 2021

Preprint

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Metro systems in megacities such as Beijing, Shenzhen and Guangzhou are under great passenger demand pressure. During peak hours, it is common to see oversaturated conditions (i.e., passenger demand exceeds network capacity), which bring significant operational risks and safety issues. A popular control intervention is to restrict the entering rate during peak hours by setting up out-of-station queueing with crowd control barriers. The out-of-station waiting can make up a substantial proportion of total travel time but is not well-studied in the literature. Accurate quantification of out-of-station waiting time is important to evaluating the social benefit and cost of service scheduling/optimization plans; however, out-of-station waiting time is difficult to estimate because it is not a part of smart card transactions. In this study, we propose an innovative method to estimate the out-of-station waiting time by leveraging the information from a small group of transfer passengers-those who transfer from nearby bus routes to the metro station. Based on the estimated transfer time for this small group, we first infer the outof-station waiting time for all passengers by developing a Gaussian Process regression with a Student-t likelihood and then use the estimated out-of-station waiting time to build queueing diagrams. We apply our method to the Tiantongyuan North station of Beijing metro as a case study; our results show that the maximum out-of-station waiting time can reach 15 minutes, and the maximum queue length can be over 3000 passengers. Our results suggest that out-of-station waiting can cause significant travel costs and thus should be considered in analyzing transit performance, mode choice, and social benefits. To the best of our knowledge, this paper is the first quantitative study for out-of-station waiting time.

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Section: Discussionmentioning

confidence: 99%

Section: Potential Solutions For Out-of-station Queueingmentioning

confidence: 99%

Quantifying out-of-station waiting time in oversaturated urban metro systems

Zhu¹,

Cheng²,

Wu³

et al. 2021

Preprint

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“…The details of the mid-term model are available in Lu et al [33]. The mid-term model was also used in past studies [34][35][36]. We describe the part of the model relevant to this research in Section 3.…”

Section: Simulation Frameworkmentioning

confidence: 99%

A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles

Alho

Sakai

et al. 2021

Future Transportation

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Time-sensitive parcel deliveries—shipments requested for delivery in a day or less—are an increasingly important aspect of urban logistics. It is challenging to deal with these deliveries from a carrier perspective. These require additional planning constraints, preventing the efficient consolidation of deliveries that is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities, such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo hitching. While there are various system designs, it is crucial that such a solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of mobility-on-demand (MOD) services that perform same-day parcel deliveries. To test the MOD-based solutions, we utilize a high-resolution agent- and activity-based simulation platform of passenger and freight flows. E-commerce demand carrier data collected in Singapore are used to characterize simulated parcel delivery demand. We explore operational scenarios that aim to minimize the adverse effects of fulfilling deliveries with MOD service vehicles on passenger flows. Adverse effects are measured in fulfillment, wait, and travel times. A case study on Singapore indicates that the MOD services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle kilometers travelled without compromising the quality of MOD for passenger travel. Insights into the operational performance of the cargo-hitching service are also provided.

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“…Among them, many platform-based ride-sharing service studies were conducted as a fare-setting model to reduce traffic demand through fare adjustment to induce waiting time and travel time, or to induce supply by improving driver profitability [26][27][28]. Furthermore, operational data from real-world mobility services enabled verification of the dynamic pricing strategy's effectiveness [29][30][31].…”

Section: Study On Dynamic Pricing In Transportation Fieldmentioning

confidence: 99%

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

et al. 2021

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The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market.

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Examining impacts of time-based pricing strategies in public transportation: A study of Singapore

Cited by 15 publications

References 24 publications

Quantifying out-of-station waiting time in oversaturated urban metro systems

Quantifying out-of-station waiting time in oversaturated urban metro systems

A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

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