Peak-avoidance Experiment

Reward, tradable perimits, health information

2020-12-21

2021-01-31

Whether subjects depart in peak hours.
Whether subjects depart during 6:45-7:45 a.m.
Whether subjects depart during 7:00-7:30 a.m.

We consturct some dependent varibales (Dummies). If subjects depart in peak hous (6:45-7:45 a.m., or 7:00-7:30 a.m.), the variables equals 1, else 0.

None

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Congestion on urban roads in megacities is increasing as the demand for trips increases and the supply of road infrastructure remains limited. Congestion is a collective, synchronic phenomenon: massive commuting at a more or less common time frame (e.g. the morning rush hour). Thus, shifting commuters’ departure times to less congested times (before or after the rush hour), in theory, lead to considerable time savings, greater travel certainty, and lower external costs of congestion.

To balance the commuting demand and road capacity and reduce congestion, this experiment focuses on using internet-based measures to nudge travelers to avoid rush hour.

Three incentives will be considered and compared:
(1) Reward system: Participants will receive a certain amount of monetary reward if they depart to avoid the peak.
(2) Tradable permits system: Participants need to use permits when departing during the peak. Before the experiment, each participant will receive a certain amount of initial permits and initial monetary budgets. They can trade the permits in an online permit market.
(3) Nudge information: Participants will receive nudge information once a week to tell them the disadvantage of peak travel.

Sample:
We will cooperate with an App company in China. Its users are car drivers from different districts of Beijing. Each user has downloaded the APP and equipped an OBD box on their cars, which can record their GPS data. The recruitment and incentives will be conducted based on the APP.

Congestion Charge (Group ACE):
• Initial Budget: Participant will receive 400 yuan (100¥ per week) in their budget before the incentive month. (The money couldn’t be withdrawn until the end of the experiment)
• Charging Rules: From Mon. to Fri., if participants depart in the morning peak, they will be charged.
- 6:45-7:00 a.m., 10 yuan
- 7:00-7:30 a.m., 20 yuan
- 7:30-7:45 a.m., 10 yuan
- Other time, no charge
• The calculation of peak avoidance ratio X% in Group A:
- Total peak points = the number of lower peak departures * 1 + the number of higher peak departures * 2
- Peak avoidance ratio = week-average total peaks during incentive weeks / historical week-average total peaks

Tradable permits (Group B):
• Initial permit price: 10 yuan
• Charging Rules: From Mon. to Fri., if participants depart in the morning peak, they need to use permits.
- 6:45-7:00 a.m., 1 permit
- 7:00-7:30 a.m., 2 permits
- 7:30-7:45 a.m., 1 permit
- Other time, no charge
• Initial Permit allocation (weekly allocate):
- Participants’ week-average peak trips * peak avoidance ratio X% (Group A’ s peak avoidance ratio)
• Initial Monetary budget (one-off payment): four week * (week-maximum permit usage – initial permit allocation) * initial permit price
• Permit price: Changing in real time (depend on cumulative purchases and sells of permits). Participants could trade permits in the APP whenever and whereever they like.

 
Nudge (Group D):
Why don't we encourage you to drive in rush hours?

Driving in peak-voidence hours is more likely to face road congestion, resulting in longer commuting time and long stay in confined space , so it is not conducive to your:

1. Body health
 In Beijing, for those over 50 years old, the weight of people who are with cars gain in five years is 10kg more than that of those without a car (Anderson et al. 2019)
 Long time driving (especially more than 1 hour / day) is more likely to cause obesity and cardiovascular disease (Sugiyama et al. 2016)
 When driving for more than 1 hour per day, the weight and waist circumference of people will increase by 2.3kg and 1.5cm (Sugiyama et al. 2016)
2. Mental health
 Compared with public transport commuting, driving commuting is more likely to lead to depression (fernchak & katirai, 2015)
 For every 10 minutes more commuting time, the risk of depression increased by 0.5% (Wang et al. 2019)

Stratified randomization by a computer

Individuals

No

1 company

500 drivers

100 subjects control, 100 subjects assigned to reward group, 100 subjects assigned to permit group, 100 subjects assigned to information group, 100 subjects both the reward and information treatment.

Specify the unit: times standard deviation: 20% percentage: 30% behavioral shift Minimum effect size: 70 of each experimental group, a total of 420 subjects are needed