The Impact of Beijing Subway’s New Fare Policy on Riders’ Attitude, Travel Pattern and Demand

Zhang, Jiechao; Yan, Xuedong; An, Meiwu; Sun, Li

doi:10.3390/su9050689

Cited by 13 publications

(11 citation statements)

References 47 publications

(49 reference statements)

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“…Moreover, the high negative impact was significant in the first month after the fare increase through a slight decrease in service utilization, while after four months the utilization level rebounded back and ended up almost in the same level before changing the fare policy. Predominantly, the new fare policy did not impact the public transport utilization long term [17].…”

Section: Literature Reviewmentioning

confidence: 93%

Evaluating Passenger Demand for Development of the Urban Transport System by an AHP Model with the Real-World Application of Amman

2019

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The demand for a service includes generally two major components; quality elements and the reasonable and affordable price. Public transport can be considered as a special service, there is no direct market competition for the provider, but the use of private transport modes substitutes the usage of public vehicles. The dominating competitor, the usage of private cars, causes higher CO2 emission and has a serious impact on the environment. Thus, it is important to analyze from market and sustainability point of view which are the preferences of the public for the improvement of the urban transport system. This paper aims to conduct this analysis by including quality criteria and transport fare criteria related to the current service of a city and by setting up and testing a generally applicable model for decision support. Since the acquisition of public preference was the primary objective, and the problem can be considered as decision making, the Analytic Hierarchy Process was selected as methodology. There are previous research results of applying this method on public transport, however, not in an integrated model, in which quality and cost considerations are pairwise compared. Thus, the conventional Analytic Hierarchy Process (AHP) technique was used and the well-proven requisites of consistency and sensitivity check were analyzed. The new model was tested in a case study: surveying the public transport demand in the capital of Jordan, Amman.

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Section: Literature Reviewmentioning

confidence: 93%

Evaluating Passenger Demand for Development of the Urban Transport System by an AHP Model with the Real-World Application of Amman

2019

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“…To represent full costs and increase use efficiency, governments often adjust prices of public good or resource, such as a public transportation system [81,82], fossil fuels [83] and power [84]. Thus, associated with water price reforms in China [85], water tariffs were thought to be an effective tool to align water demand with supply and provide funding for the MRC restoration.…”

Section: Valuation Of Restoring Esmentioning

confidence: 99%

Estimation of Rural Households’ Willingness to Accept Two PES Programs and Their Service Valuation in the Miyun Reservoir Catchment, China

Yang

Zhang

et al. 2018

Sustainability

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Abstract:As the only surface water source for Beijing, the Miyun Reservoir and its catchment (MRC) are a focus for concern about the degradation of ecosystem services (ES) unless appropriate payments for ecosystem services (PES) are in place. This study used the contingent valuation method (CVM) to estimate the costs of two new PES programs, for agriculture and forestry, and to further calculate the economic value of ES in the MRC from the perspective of local rural households' willingness to accept (WTA). The results of Logit model including WTA and the variables of household and village indicate that the local socio-economic context has complex effects on the WTA of rural households. In particular, the bid amount, location and proportion of off-farm employment would have significant positive effects on the local WTA. In contrast, the insignificance of the PES participation variable suggests that previous PES program experiences may negatively impact subsequent program participation. The mean WTA payments for agriculture and forestry PES programs were estimated as 8531 and 8187 yuan/ha/year, respectively. These results consistently explain the differentiated opportunity costs on both farmland and forestry land. Meanwhile, the differentiated WTA values in Beijing vs. the surrounding Hebei Province follow the interest differences and development gaps between jurisdictions. Finally, the total economic value of ES in the MRC area was estimated at 11.1 billion yuan/year). The rational economic value of ES for the restoration priority areas reaches 515.2 million yuan/year. For the existing budget gap (299 million yuan/year), the study proposed that decision makers increase the water tariff by 0.08 yuan to raise the funds needed. The study also concluded that these results are not only financially and politically feasible but also cost-effective. This study has policy implications for improving the implementation efficiency and providing quantified supports for PES programs in megacity source water area.

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“…Figure 2 shows the distribution of Beijing Subway smart cards and transaction records in 2016. The average number of trips (number of transaction records/smart cards) a subway user makes in a day is about 1.74 on workdays, which is extremely stable from Monday to Thursday and a little higher on Friday [22,32].…”

Section: Data Foundation Of the Beijing Subwaymentioning

confidence: 99%

“…Moreover, personal characteristics and preferences are also related to the mode and choice of commuting travel, such as age, household income, and occupation level [14,25,29]. Additionally, fare policy has a significant impact on the user's degree of satisfaction, but not the passenger flow volume [30][31][32]. Ridership statistics and performance indicators mentioned in these studies offer helpful information to determine the principles of SIUs used in this paper.…”

Section: Introductionmentioning

confidence: 99%

Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

et al. 2018

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Urban rail transit has become an indispensable option for Beijing residents. Subway inelastic users (SIUs) are the main component among all users. Understanding the proportion of SIUs and their characteristics is important in developing service promotions and helpful for subway agencies in making marketing policies. This paper proposes a novel and simple identification process for identifying regular subway inelastic trips (SITs) in order to distinguish SITs and non-SITs and extract their characteristics. Weekly station sequence (WSS) is selected as the data-based format, principles of SIUs are discussed and chosen, and the framework of SIT identification is applied to a large weekly sample from the Beijing Subway. A revealed preference (RP) survey and results analysis are undertaken to estimate the performance of the proposed methods. The RP survey validation shows that accuracy reaches as high as 94%, and the distribution analysis of SITs and their origin-destinations (ODs) indicate that the SIT characteristics extracted are consistent with the situation in Beijing. The proportion of SIUs is stable on workdays and is more than 80% during rush hour. The efforts described in this paper can provide subway managers with a useful and convenient method to understand the characteristics of subway passengers and the performance of a subway system.

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The Impact of Beijing Subway’s New Fare Policy on Riders’ Attitude, Travel Pattern and Demand

Cited by 13 publications

References 47 publications

Evaluating Passenger Demand for Development of the Urban Transport System by an AHP Model with the Real-World Application of Amman

Evaluating Passenger Demand for Development of the Urban Transport System by an AHP Model with the Real-World Application of Amman

Estimation of Rural Households’ Willingness to Accept Two PES Programs and Their Service Valuation in the Miyun Reservoir Catchment, China

Identification of Inelastic Subway Trips Based on Weekly Station Sequence Data: An Example from the Beijing Subway

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