Research Article

Do the Effects of ICT Use on Trip Generation Vary across Travel Modes? Evidence from Beijing

Table 1

Variable definition and statistical description.

VariableDescriptionCase/meanPercentage/S.E.

Dependent variables
Any auto trips1 = respondent generates auto trips; 0 = otherwise75173.48
Auto tripsThe number of trips completed by car in the past week3.406.05
Any transit trips1 = respondent generates transit trips; 0 = otherwise86384.44
Transit tripsThe number of trips completed by the transit public in the past week5.4016.49
Any active trips1 = respondent generates active trips; 0 = otherwise91289.24
Active tripsThe number of trips completed by walking/bicycling in the past week7.2710.75

Built environment attributes
Core zone1 = if the respondent lives within the core functional district; 0 = otherwise1009.78
Extended zone1 = if the respondent lives within the urban function extended district; 0 = otherwise44943.93
Development zone1 = if the respondent lives within the new urban development district; 0 = otherwise30329.65
Ecological zone1 = if the respondent lives within the ecological conservation district; 0 = otherwise17016.63
Distance to the nearest bus stop1 = distance below 300 m41040.12
2 = distance between 301 and 500 m27326.71
3 = distance between 501 and 1000 m25324.76
4 = distance between 1001 and 2000 m757.34
5 = distance beyond 2000 m111.08
Distance to the nearest metro station1 = distance below 300 m767.44
2 = distance between 301 and 500 m12512.23
3 = distance between 501 and 1000 m23723.19
4 = distance between 1001 and 2000 m24824.27
5 = distance beyond 2000 m33632.88
Distance to the nearest sport facility1 = distance below 300 m34033.27
2 = distance between 301 and 500 m26125.54
3 = distance between 501 and 1000 m22522.02
4 = distance between 1001 and 2000 m10710.47
5 = distance beyond 2000 m898.71
Distance to the nearest garden1 = distance below 300 m15815.46
2 = distance between 301 and 500 m16916.54
3 = distance between 501 and 1000 m24924.36
4 = distance between 1001 and 2000 m21220.74
5 = distance beyond 2000 m23422.90
Distance to the nearest restaurant1 = distance below 300 m48247.16
2 = distance between 301 and 500 m27226.61
3 = distance between 501 and 1000 m17316.93
4 = distance between 1001 and 2000 m686.65
5 = distance beyond 2000 m272.64
Distance to the nearest mall1 = distance below 300 m615.97
2 = distance between 301 and 500 m12812.52
3 = distance between 501 and 1000 m21721.23
4 = distance between 1001 and 2000 m27827.20
5 = distance beyond 2000 m33833.07

Attitudes
Driving1 = strongly disagree282.74
2 = disagree a little11210.96
3 = neither agree nor disagree47546.48
4 = agree a little28227.59
5 = strongly agree12512.23
Public transport1 = strongly disagree131.27
2 = disagree a little656.36
3 = neither agree nor disagree29528.86
4 = agree a little39738.85
5 = strongly agree25224.66
Walking1 = strongly disagree40.39
2 = disagree a little383.72
3 = neither agree nor disagree22522.02
4 = agree a little38137.28
5 = strongly agree37436.59

Individual/household characteristics
Gender1 = male; 0 = female50.100.50
AgeAge in years33.889.49
Income1 = less than 3000 RMB/month959.30
2 = 3001–4500 RMB/month10310.08
3 = 4501–6000 RMB/month14514.19
4 = 6001–8000 RMB/month18618.20
5 = 8001–10000 RMB/month17517.12
6 = 10001–15000 RMB/month18418.00
7 = more than 15000 RMB/month13413.11
Education level1 = having a college degree; 0 = having no college degree78.400.41
Household sizeNumber of persons in the household2.891.29

Internet use characteristics
Time spent onlineAverage time spent online for nonwork purposes4.012.92
Frequency of e-shopping1 = never706.85
2 = 1-2 times a month23422.90
3 = 1-2 times a week38037.18
4 = 2-3 times a week22922.41
5 = every day10910.67
Frequency of ordering food online1 = never27026.42
2 = 1-2 times a month20219.77
3 = 1-2 times a week26225.64
4 = 2-3 times a week19619.18
5 = everyday929.00