- freely available
Sustainability 2020, 12(2), 620; https://doi.org/10.3390/su12020620
2.1. Joint RP and SP MNL Model
2.2. Choice Set Restriction
- Car mode is not available if a traveler does not have a household private car.
- Taxi mode is available for all the travelers.
- If the rail/bus in-vehicle time from the zone-to-zone skim matrices (see Section 3.2) takes a missing value because the trip origin or destination does not have good access to rail/bus station (the cut-off access distance for bus station is set to 2 km, and for rail is set to 5 km), the Rail/Bus mode is not available for the traveler in this trip.
- If either of the in-vehicle times of rail and bus take a missing value, Rail & Bus mode is not available for the traveler in this trip.
- According to the cumulative distribution curve of trip distance of non-motorized trips derived from the Shanghai transport survey report , 99% of non-motorized trips are less than 25 km. We assumed that non-motor mode is not available if non-motor trip distance exceeds 25 km.
- In the survey, only a small part of the commuters who are willing to shift to carsharing do not have a driver’s license yet, but they are still potential users of carsharing. And it is not difficult to obtain a driver’s license in China. For this consideration, carsharing mode is assumed available for all the travelers.
3. Data Sources and Description
3.1. Web-Based Travel Survey on Carsharing
- The first part (RP part) collected full-mode commute trip information, including origin and destination locations (ODs), current commute mode, travel modes of accessing and egressing public transit stations, trip beginning and ending times, and the number of companions.
- The second part (SP part) gathered a potential mode shift to EVCARD under four hypothetical scenarios. First, the basic operational characteristics and usage process of EVCARD were briefly introduced to respondents, as shown in Figure 2. Then, four hypothetical scenarios were designed based on carsharing’s unit price to test price sensitivity, which were 0.4, 0.6, 0.8, and 1.0 RMB Yuan/min, respectively. We conducted a pre-survey in May 2018 with a sample size of 77, and four SP unit price levels were designed as 0.2, 0.4, 0.6, and 0.8 RMB Yuan/min based on the actual unit price of EVCARD in Shanghai, which is 0.6 RMB Yuan/min. The analysis of the pre survey showed that almost all respondents were willing to shift to carsharing when the unit price is 0.2 RMB Yuan/h, which means that we have assumed a relatively low price setting. Thus, we designed four unit price levels starting from 0.4 RMB Yuan/min with an increment of 0.2 RMB Yuan/min in order to have an adequate price variance as well as the exact unit price in SP scenarios. Respondents answered whether they would like to shift to EVCARD for commute under two hypothetical scenarios that appeared at equal probability randomly, which could not only reduce the complexity and improve the quality of questionnaire, but also collect information in scenarios with different levels of carsharing’s unit price. Thus, the final sample comprises a total of 3229 observations (887 RP observations and 2342 SP observations).
- The third part collected the socioeconomic and demographic characteristics of commuters, including gender, age, education, personal monthly income, marital status, residence type, household member, household vehicle ownership, car purchase plan, and familiarity with EVCARD and so on.
3.2. Zone-to-Zone Skim Matrices
3.3. Sample Description
3.3.1. OD Distribution
3.3.2. Mode Shares and Mode Shifts
3.3.3. Level-of-Service Attributes
3.3.4. Socioeconomic and Demographic Attributes
4. Empirical Results
4.1. Model Estimation Results
- The coefficient of carsharing fare is significantly negative, implying that commuters are not inclined to choose carsharing when the fare increases. Since the carsharing fare is perfectly correlated with its in-vehicle time, the utility function only contains the fare variable with a coefficient of −0.0749. VOT can also be known as how much travelers are willing to pay to reduce travel time, i.e., the value of travel time savings (VTTS). Estimation of VOT can support the policy designs of transport operators and hence make adjustments on prices and levels of service offered. Because the driving characteristics of the carsharing are identical with those of the car, the coefficient of car in-vehicle time, which is −0.0444, can be borrowed to calculate the VOT of carsharing. Thus, the VOT of carsharing is 35.56 RMB Yuan (5.08 US Dollar)/h calculated by the ratio between the coefficients of car in-vehicle time and carsharing fare, which is less than the sample average hourly wage of 61 RMB Yuan (8.71 US Dollar)/h (the average monthly income of the sample is 10.6 K RMB Yuan, and the normal working hours per year is 2080 h) and is within a reasonable range. The result in this paper is consistent with that in Li’s research , but smaller than that in De Luca . In the former research in Taiyuan, China, the VOT is 22.0 RMB Yuan (3.14 US Dollar)/h in mid-distance (2–5 km) trip and 81.1 RMB Yuan (11.59 US Dollar)/h in long-distance (more than 5 km) trip. The VOT value in this paper is estimated without distinguishing the distance, thus it is between the two values. In the latter research in Salerno, Italy, the VOT is 10 Euros (11.18 US Dollar)/h. Also, Wang et al.  derived a VOT value of 9.06 US Dollar/h for the carsharing service in Seattle. It can be seen that the VOT value varies widely among countries because of different monetary and transportation backgrounds.
- The more companions, the less cost per person shares, the more willing commuters are to use carsharing. Similarly, the coefficients of the number of companions in taxi utility functions is also positive.
- Unlike previous studies in San Francisco , Basel, Switzerland , Shanghai , and Beijing , women in this study and studies in Peshawar, Pakistan , and Salerno, Italy  are found more willing to use carsharing, probably because most commuters in the sample come from families with cars, where men are often main car users. In this case, women may prefer to use carsharing if they want to use a motorized private commute mode.
- The age term and age squared term indicate that the probability of commuters choosing carsharing increases with age and peaks at 26 years old. This is consistent with the results obtained from the studies in London , Greece , and Shanghai [32,33], that younger people are more willing to accept carsharing. Young people at this age have just started to work and probably cannot afford to buy cars, but they have a high demand for commute mobility. Thus, carsharing becomes a good choice for them. As commuters’ age increases, the probability of choosing carsharing gradually decreases, probably because they can afford to buy cars and need to allocate more expenditure to household consumption.
- Carsharing is more attractive to high-income groups, possibly because they are less sensitive to the cost. The studies in Basel, Switzerland , London , Puget Sound , and Beijing  also drew similar conclusions. But the study in Greece  showed that carsharing was more appealing to low- and middle-income groups.
- Commuters with infants or preschool children at home are more willing to use carsharing, presumably because they need to pick up spouses or children, and carsharing can meet the needs for travel flexibility and convenience.
- People who have a car purchase plan within one or two years have an urgent need for motorized travel, therefore they prefer to choose carsharing to meet the current travel needs before purchasing vehicles.
- The coefficient of familiarity with EVCARD is positive, and commuters are more willing to use carsharing with the increase of the degree of familiarity. Carsharing operating companies should enhance advertising and consider taking measures to lower the threshold for the first use of carsharing.
- Commuters with Shanghai C licensed cars are restricted to travel only in the suburbs of Shanghai, and commuters with motorcycles are also restricted from traveling on certain roads and areas, and their travel is greatly affected by weather and environment. These two types of commuters prefer to choose carsharing with unrestricted travel privilege and comfortable sheltered environment. Conversely, the ownership of private bicycles may indicate that commuters’ short-distance travel needs have been met, so they are reluctant to spend more and do not need to shift to carsharing.
- Parking space will be scarcer when the population density in origin (O) zone is higher. When the job density in destination (D) zone is higher, it shows that the zone is likely to belong to a commercial district and parking fees will be more expensive. In these cases, commuters are more willing to shift carsharing with exclusive but free parking spaces. Additionally, it was found that carsharing was more attractive to individuals residing in higher-density areas in Le Vine’s  and Dias’s  researches. Similarly, the coefficient of job density variable in D zone is also negative in the car utility function.
- The coefficient of car’s in-vehicle time is less than that of taxi. They are respectively 2.71 and 2.57 times as much as that of rail and of bus. It indicates that people’s tolerance for car’s in-vehicle time is higher than that of taxi, but significantly smaller than that of public transit. It is probably because drivers need to concentrate on driving and are prone to fatigue, but commuters taking public transit can use smartphones to entertain or relax, which reduces their sensitivity to time.
- Accessibility to public transit stations is an important factor influencing commuters’ public transit choice probability, and long access/egress distance to the station will discourage commuters from choosing public transit.
- The initial waiting time, transfer waiting time, and transfer walking time of bus have a greater negative impact on the probability of commuters choosing bus than in-vehicle time. Because waiting and transferring environment of bus is not comfortable, and commuters are generally in a state of anxious waiting or in a hurry. Similarly, excessive transfers and overlong waiting time of Rail & Bus will reduce the probability of commuters choosing this mode.
- Unlike carsharing and taxi, the higher the number of companions on the commute way, the lower the probability of choosing bus and non-motor modes. It is inconvenient for many people to use bus and non-motor modes together, and the cost or resources required are proportional to the number of their companions in these two modes, unlike in motor vehicles.
- In the car mode, commuters who are between 26 and 35 years old, full-time workers, living with children, tend to use private car most because they need to go home in time to take care of their children or take them to and from school. Thus, they have a high demand for mobility and prefer to commute by car. In addition, the costs of car purchase, fuel consumption, and maintenance are high, it is, therefore, more suitable for the long-term use of high-income groups.
- Women with higher education years and income are more willing to use taxis to commute. They have higher requirements for travel comfort and are insensitive to travel costs.
- Women are more likely to commute by public transit, possibly because men in the family are usually the main user of private cars, and most families in Shanghai own only one private car. Commuters with preschool or schoolchildren are not inclined to choose public transit, presumably because they generally need to pick up their children and private mode is more convenient. Since married people will consider choosing a convenient and time-saving travel mode to take care of family members, they are unwilling to choose Rail & Bus.
- The commuters who are well educated are more willing to use rail. The reason may be that these people usually work in white-collar jobs, and their work places are concentrated in urban or commercial areas being well covered by rail stations. Full-time workers and full-time students are more willing to commute by rail due to time and speed requirements.
- Full-time students (mainly high school and university students) are more likely to use non-motorized travel mode, possibly because they mostly walk to school or live in school.
4.2. Elasticity and Marginal Effect Analysis
5. Conclusions and Discussions
Conflicts of Interest
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|Car||In-vehicle time (min)||345||23.20||16.20|
|Taxi||In-vehicle time (min)||21||18.50||17.90|
|Fare (RMB Yuan 1)||21||28.58||33.24|
|Rail||In-vehicle time (min)||192||20.51||13.04|
|Fare (RMB Yuan)||192||3.94||0.84|
|Access distance (km)||192||1.34||0.83|
|Egress distance (km)||192||1.11||0.74|
|Initial waiting time (min)||192||2.15||0.28|
|Transfer waiting time (min)||192||1.24||1.45|
|Number of transfers||192||0.57||0.65|
|Bus||In-vehicle time (min)||121||30.19||24.83|
|Fare (RMB Yuan)||121||2.89||1.29|
|Access distance (km)||121||0.58||0.42|
|Egress distance (km)||121||0.66||0.43|
|Initial waiting time (min)||121||3.84||1.81|
|Transfer waiting time (min)||121||1.66||2.81|
|Transfer walking time (min)||121||0.65||2.02|
|Number of transfers||121||0.45||0.64|
|Rail & Bus||Total in-vehicle time (min)||18||53.41||24.51|
|Fare (RMB Yuan)||18||6.00||1.57|
|Rail in-vehicle time (min)||18||30.44||18.12|
|Bus in-vehicle time (min)||18||22.96||22.14|
|Access distance (km)||18||0.53||0.43|
|Egress distance (km)||18||0.84||0.44|
|Initial waiting time (min)||18||4.28||3.20|
|Transfer waiting time (min)||18||5.08||3.14|
|Transfer walking time (min)||18||3.84||2.94|
|Number of transfers||18||1.94||0.87|
|Non-Motor||Trip distance (km)||190||3.79||4.04|
|New Mode in SP Scenarios|
|Carsharing||Fare (RMB Yuan)||Scenario 1||292||10.49||6.79|
|Description of Discrete Variables|
|Age (Years) 1||Residence type|
|51–60||1.47%||Infant (0–3 years old)||14.21%|
|≥61||0.11%||Preschooler (4–6 years old)||16.57%|
|Education||Schoolchild (7–12 years old)||20.63%|
|Elementary||0.45%||Teenager (13–18 years old)||7.67%|
|Junior high school||0.56%||Grown-up (19–60 years old)||99.89%|
|Senior high school||3.16%||The elderly (61–70 years old)||12.74%|
|Technical secondary school||1.47%||The elderly (71–80 years old)||3.04%|
|Junior College||11.05%||The elderly (≥81 years old)||1.13%|
|Bachelor degree||68.55%||Household vehicle ownership|
|Master degree||13.08%||Urban licensed car||48.48%|
|Doctoral degree||1.69%||Shanghai C licensed car 3||11.05%|
|Job category||Other cities’ licensed car 3||13.98%|
|Full-time job||92.57%||Employer-provided car||4.06%|
|Part-time job||1.60%||Electric bicycle||30.78%|
|Full-time student||4.80%||Private bicycle||28.07%|
|Personal monthly income (RMB Yuan 2)||None||16.57%|
|Car purchase plan|
|≤2 K||4.62%||Within 1 year||14.54%|
|2 K–4.5 K||4.96%||Within 1–2 years||20.52%|
|4.5 K–6 K||10.82%||Within 2–3 years||14.43%|
|6 K–8 K||18.49%||Within 3–5 years||10.48%|
|8 K–10 K||21.08%||No plan within 5 years||17.14%|
|10 K–15 K||22.77%||Not sure||22.89%|
|15 K–20 K||9.13%||Familiarity with EVCARD|
|20 K–30 K||5.30%||Never heard of||22.10%|
|>30 K||2.82%||Heard of but not used||62.34%|
|Driving license||Used but not often||12.74%|
|Description of Continuous Variables|
|Years of education 4||16.11||1.83|
|Personal monthly income (10 K RMB Yuan) 5||1.06||0.65|
|Population density (1 K people/km2) 6||In O Zone||2.84||2.35|
|In D Zone||1.86||2.08|
|Job density (1 K positions/km2) 6||In O Zone||1.45||2.19|
|In D Zone||2.50||2.75|
|In-vehicle time (min)||−0.0444||0.0049||−9.12|
|Personal monthly income (10 K RMB Yuan 1)||0.6222||0.0885||7.03|
|Living with an infant (0–3 years old)||0.4235||0.1428||2.97|
|Living with a preschooler (4–6 years old)||0.2516||0.1390||1.81|
|Job density in D zone (1 K positions/km2)||−0.0613||0.0193||−3.18|
|Taxi2||In-vehicle time (min)||−0.0604||0.0123||−4.90|
|Number of companions||0.1990||0.0620||3.21|
|Years of education||0.2704||0.0808||3.35|
|Personal monthly income (10 K RMB Yuan)||1.4022||0.2084||6.73|
|In-vehicle time (min)||−0.0164||0.0049||−3.33|
|Access/Egress distance (km)||−0.5257||0.0536||−9.81|
|Years of education||0.1805||0.0323||5.59|
|Living with a preschooler (4–6 years old)||−0.9861||0.2034||−4.85|
|Living with a schoolchild (7–12 years old)||−0.9873||0.1583||−6.23|
|In-vehicle time (min)||−0.0173||0.0035||−4.95|
|Access/Egress distance (km)||−0.4520||0.1079||−4.19|
|Initial waiting time (min)||−0.0501||0.0300||−1.67|
|Transfer waiting time (min)||−0.1679||0.0326||−5.16|
|Transfer walking time (min)||−0.1079||0.0459||−2.35|
|Number of companions||−0.3627||0.0925||−3.92|
|Living with a schoolchild (7–12 years old)||−0.5101||0.1723||−2.96|
|Rail & Bus||Constant||7.1995||1.4935||4.82|
|Bus in-vehicle time (min)||−0.0173||0.0035||−4.95|
|Access/Egress distance (km)||−0.4774||0.2261||−2.11|
|Transfer waiting time (min)||−0.1330||0.0479||−2.78|
|Trip distance (km)||−0.2922||0.0215||−13.61|
|Number of companions||−0.3378||0.0693||−4.87|
|Fare (RMB Yuan)||−0.0749||0.0069||−10.91|
|Number of companions||0.0895||0.0377||2.37|
|Personal monthly income (10 K RMB Yuan)||0.8355||0.1520||5.50|
|Living with an infant (0–3 years old)||0.3978||0.1491||2.67|
|Living with a preschooler (4–6 years old)||0.4196||0.0940||4.46|
|Car purchase plan within 1 year||0.9385||0.1408||6.67|
|Car purchase plan within 1–2 years||0.6679||0.1194||5.60|
|Has heard of but not used EVCARD||0.7957||0.1283||6.20|
|Has used EVCARD but not often||1.2567||0.1788||7.03|
|Has used EVCARD frequently||1.8747||0.3288||5.70|
|Owning a household Shanghai C licensed car||0.4607||0.1490||3.09|
|Owning a household motorcycle||1.0020||0.3489||2.87|
|Owning a household private bicycle||−0.2074||0.1047||−1.98|
|Population density in O zone (1 K people/km2)||0.0406||0.0201||2.02|
|Job density in D zone (1 K positions/km2)||0.0468||0.0188||2.49|
|Scaling parameter||1.1114||0.0672||1.66 3|
|Number of observations: RP 887 + SP 2342 = 3229|
|Elasticities of Continuous Variables (%ΔP)|
|Personal monthly income||+0.072||+1.148||−0.323||−0.355||−0.358||−0.322||+0.281|
|Job density in D zone||−0.118||−0.019||−0.018||−0.017||−0.009||−0.019||+0.079|
|Marginal Effects of Discrete Variables (ΔP)|
|Number of companions||−0.0028||+0.0019||+0.0007||−0.0243||−0.0001||−0.0003||+0.0248|
|Car purchase plan within 1 year||−0.0665||−0.0037||−0.0401||−0.0260||−0.0048||−0.0472||+0.1882|
|Car purchase plan within 1–2 years||−0.0472||−0.0026||−0.0279||−0.0183||−0.0034||−0.0331||+0.1324|
|Has heard of but not used EVCARD||−0.0583||−0.0033||−0.0348||−0.0228||−0.0042||−0.0417||+0.1651|
|Has used EVCARD but not often||−0.0922||−0.0050||−0.0566||−0.0362||−0.0067||−0.0665||+0.2632|
|Has used EVCARD frequently||−0.1328||−0.0070||−0.0847||−0.0527||−0.0097||−0.0966||+0.3835|
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