Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China
Abstract
:1. Introduction and Background
2. Data and Methods
2.1. Study Area
2.2. Data Collection
- In Stage 1, we explained to the participants what PTI was and asked if they were prone to use it if available. The participants who were willing to use PTI were defined as potential users and selected as the TIAS participants. The sample size of TIAS was 21,602 participants.
- In Stage 2, the motorized potential users (2229 participants) who habitually travel by car were selected. We then invited them to respond to the hypothetical PTI as shown in Table 1. We chose the motorized potential users because their possible mode shift under PTI would contribute to the reduction of auto use and help optimize the travel mode split.
- In Stage 3, 622 participants who chose to “Change modes” in Stage 2 were selected to further respond to three different hypothetical scenes with more detailed context. In this stage, the participants were assumed to take three daily trips that required high travel time reliability. The three trips were different in terms of trip length (short, medium, and long). Before the trips, they would receive the same PTI as in Stage 2 and additional information about alternative modes differentiated from travel time and cost (Table 1). They may choose alternative modes or stick to driving a car. The travel time and cost in Stage 3 was abstracted from the HTS.
2.3. Cluster Analysis
Details of the PTI in Stage 2 & 3 | |||
Form | Short message service (SMS) | ||
Cost | Free | ||
Content | Unexpected severe traffic congestion just occurred on the route where you were going to travel through, which would cause delay and potentially make you late for the trip. | ||
Options | 1 = Change departure time; 2 = Change route; 3 = Change modes; 4 = Change destination; 5 = Cancel the commute; 6 = No change. | ||
Options in three scenes of Stage 3 | |||
Scene | Options | Estimated Travel time (min) | Estimated Travel cost (Renminbi, 6.21 Renminbi ≈ 1 US Dollar ) |
Scene 1 (1 km) | Walking | 15 | 0 |
Bicycle | 5 | 0 | |
Bus | 10 | 2 | |
Motorcycle or moped | 3 | 0.25 | |
No change | - | - | |
Scene 2 (4 km) | Elevated light rail * | 14 | 2 |
Bicycle | 20 | 0 | |
Bus | 18 | 2 | |
Motorcycle or moped | 12 | 1 | |
No change | - | - | |
Scene 3 (8 km) | Elevated light rail * | 20 | 4 |
Bicycle | 40 | 0 | |
Bus | 28 | 2 | |
Motorcycle or moped | 24 | 2 | |
No change | - | - |
Variable | Definition | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Age (in years) | 1 = 18–24 | 2 = 25–34 | 3 = 35–44 | 4 = 45–59 | 5 = over 59 | |||||||||||
Occupation | 1 = Government administrative | 4 = Sales or service | ||||||||||||||
2 = Enterprises and institutions | 5 = Manufacturing, construction or farming | |||||||||||||||
3 = Self-employed | 6 = Others | |||||||||||||||
Monthly income (in Renminbi) | 1 =< 370 | 2 = 371–770 | 3 = 771–1200 | 4 = 1201–2000 | ||||||||||||
5 = 2001–3000 | 6 = 3001–4000 | 7 = 4001–5000 | 8 => 5001 and more | |||||||||||||
Monthly travel cost (in Renminbi) | 1 =< 50 | 2 = 51–100 | 3 = 101–200 | 6 = >401 | ||||||||||||
4 = 201–300 | 5 = 301–400 | |||||||||||||||
Weekly work hours (hours) | 1 =< 30 | 2 = 31–40 | 3 = 41–50 | 4 = 51–60 | 5 = >61 |
3. Results
3.1. Stage 1: Socio-Demographics of Potential Users of PTI
Category | General Population | Potential Users | Differences Between General Population And Potential Users |
---|---|---|---|
Sex | |||
Male | 50.42% | 76.45% | +51.63% |
Female | 49.58% | 23.55% | –52.50% |
Total | 100.00% | 100.00% | |
Age | |||
<30 | 34.39% | 22.10% | –35.74% |
30-59 | 55.50% | 67.21% | +21.10% |
>=60 | 13.40% | 14.32% | +6.87% |
Total | 100.00% | 100.00% | |
Occupation | |||
Government administrative | 3.31% | 4.38% | +32.63% |
Enterprises and institutions | 18.94% | 24.07% | +27.09% |
Self-employed | 15.83% | 19.90% | +25.71% |
Sales or service | 6.50% | 7.27% | +11.85% |
Manufacturing, construction or farming | 20.31% | 23.49% | +15.66% |
Others | 6.42% | 7.91% | +23.21% |
Student | 11.89% | 0.88% | –92.60% |
Retiree/Unemployed | 16.80% | 12.09% | –28.04% |
Total | 100.00% | 100.00% | |
Monthly income | |||
<1200 (Low income) | 43.50% | 32.46% | –25.38% |
1200–3000 (Middle income) | 46.75% | 54.18% | +15.89% |
>3000 (High income) | 9.75% | 13.37% | +37.13% |
Total | 100.00% | 100.00% | |
Habitual Mode of Daily Trips | |||
Walk | 12.08% | 9.38% | –22.41% |
Bicycle | 13.69% | 8.56% | –37.47% |
Moped | 8.84% | 9.45% | +6.95% |
Bus | 5.86% | 4.23% | –27.86% |
Motorcycle | 46.50% | 52.69% | +13.33% |
Automobile | 10.45% | 13.42% | +28.42% |
Others | 2.58% | 2.27% | –12.02% |
Total | 100.00% | 100.00% |
3.2. Stage 2: Motorized Potential Users’ Feasible Travel Responses to PTI
3.3. Stage 3
3.3.1. Motorized Potential Users’ Modal Shift in Three Scenes of Stage 3
Scene | Options | Percentage |
---|---|---|
Scene 1 (1 km) | Walking | 36.33% |
Bicycle | 12.22% | |
Bus | 9.00% | |
Motorcycle or moped | 31.03% | |
No change | 11.41% | |
Scene 2 (4 km) | Elevated light rail | 24.44% |
Bicycle | 5.31% | |
Bus | 14.95% | |
Motorcycle or moped | 44.21% | |
No change | 11.09% | |
Scene 3 (8 km) | Elevated light rail | 48.55% |
Bicycle | 1.45% | |
Bus | 11.74% | |
Motorcycle or moped | 27.01% | |
No change | 11.25% |
3.3.2. Cluster Analysis of Motorized Potential Users Who Would Change Modes under PTI
Attributes | Cluster A | Cluster B | Cluster C | Cluster D | Cluster E |
---|---|---|---|---|---|
Category | High-income easy-working | High-income hard-working | Young white collar | Young blue collar | Low-income |
No. of participants | 330 | 104 | 84 | 87 | 17 |
Age | 35–44 (45.15%) | 45–59 (38.46%) | 25–34 (42.86%) | 25–34 (52.87%) | 45–59 (70.59%) |
Occupation (Top two categories above average are listed) | Enterprises and institutions (39.09%) Self-employed (35.45%) | Self-employed (56.73%)/ Manufacturing, construction or farming (6.73%) | Enterprises and institutions (39.29%)/ Government administrative (25.00%) | Others (82.76%) /Manufacturing, construction or farming (11.49%) | Manufacturing, construction or farming (35.29%)/ Others (17.65%) |
Monthly income (Renminbi) | >3000 (71.52%) | >3000 (54.81%) | 1200–3000 (57.14%) | 1200–3000 (55.17%) | <1200 (52.94%) |
Monthly travel cost (Renminbi) | >300 (96.36%) | >300 (77.88%) | 100–300 (67.86%) | >300 (92.86%) | 100–300 (52.94%) |
Weekly work hours (hrs) | <40 (39.39%) | >50 (48.08%) | 40–50 (40.48%) | 40–50 (60.92%) | 40–50 (47.06%) |
3.3.3. Modal Shift across Five Clusters in Response to PTI
- Scene 1: Short Distance Commute
- Scene 2: Medium Distance Commute
- Scene 3: Long Distance Commute
4. Discussion and Policy Implications
4.1. Identification of Potential Users’ Socio-Demographics
- Sex: Males covered 76.45% of potential users, a great increase from the general population. Male users are more “wired” and technologically savvy, having higher than average Internet and mobile communications use. Therefore, males can be expected to perceive less difficulty in using PTI. It indicates that male consist of a crucial market segment of PTI in Zhongshan, consistent with findings in previous literature [3].
- Age: The young-and-middle-aged group (age from 25 to 59) was most interested in PTI, whereas the younger group (age less than 25) was just the opposite. The age group of major potential users in Zhongshan is similar to those of actual users in Los Angeles (64%) and Seattle (54%) [8].
- Occupation: All working groups’ propensity to utilize PTI is high, while non-working groups, such as students, retirees and the unemployed show less interest. Working groups tend to be the so-called “control seeker” segment in commute trips, who are drawn to PTI due to the desire of greater than average control over their daily trips and better knowledge of their travel times, especially in congested travel environments [13]. The possible benefit of reducing travel time and enhancing travel time reliability brought by PTI reinforces the control ability, which attracts working group as potential users.
- Monthly Income: The propensity to utilize PTI grew simultaneously with the growth of income. The threshold is 1200 RMB, which is also the threshold of middle income in Zhongshan. According to a study also in Guangdong Province, China, travelers with middle-to-high income have higher value of travel time than low income counterparts, especially in commute trips [32]. Therefore, the middle-and-high income groups are very likely to treasure more the value of commute time, which can be enhanced by using PTI. This is in compliance with previous research findings that upper income travelers are more likely than others to use travel information [8].
- Habitual Travel Mode: In Zhongshan, private motorized (motorcycle and automobile) commuters are more interested in PTI than non-motorized ones. In recent years, they suffered from worsening traffic congestions more often, which were related to longer commuting time and lower commuting time reliability [26]. Therefore, private motorized commuters will be more likely to benefit from PTI than non-motorized ones.
4.2. Modal Shift Characteristics by Different Clusters under PTI
4.2.1. Cluster A: The High-Income and Short Working Hours
4.2.2. Cluster B: The High-Income and Long Working Hours
4.2.3. Cluster C: The Young White-Collar
4.2.4. Cluster D: The Young Blue-Collar
4.2.5. Cluster E: The Low-Income
4.3. Policy Implications
- Choosing young-to-middle-aged (25–59) and middle-to-high income male that habitually traveled by motorized modes as the target groups for the market promotion of PTI.
- Providing traveler information related to route guide and availability of alternative travel modes to general PTI users under congestions or emergencies to facilitate their change of travel route or modes.
- Providing specific traveler information to different clusters of motorized potential users according to their different responses to PTI, so as to further encourage their compliance to the information and promote them to shift to sustainable modes.
5. Strengths and Limitations
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Zhang, Y.; Yang, X.; Liu, Q.; Li, C. Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China. Sustainability 2015, 7, 5857-5874. https://doi.org/10.3390/su7055857
Zhang Y, Yang X, Liu Q, Li C. Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China. Sustainability. 2015; 7(5):5857-5874. https://doi.org/10.3390/su7055857
Chicago/Turabian StyleZhang, Yi, Xiaoguang Yang, Qixing Liu, and Chaoyang Li. 2015. "Who Will Use Pre-Trip Traveler Information and How Will They Respond? Insights from Zhongshan Metropolitan Area, China" Sustainability 7, no. 5: 5857-5874. https://doi.org/10.3390/su7055857