Journal of Advanced Transportation

Journal of Advanced Transportation / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 6383097 | https://doi.org/10.1155/2019/6383097

Ge Gao, Zhen Wang, Xinmin Liu, Qing Li, Wei Wang, Junyou Zhang, "Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data", Journal of Advanced Transportation, vol. 2019, Article ID 6383097, 18 pages, 2019. https://doi.org/10.1155/2019/6383097

Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

Academic Editor: Jose E. Naranjo
Received18 Dec 2018
Accepted24 Feb 2019
Published11 Mar 2019

Abstract

Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

1. Introduction

It is axiomatic that a model can never be better than the data from which it is estimated [1]. Household traffic surveys are mainly used in transport planning and urban planning. In early days, household traffic surveys are customarily conducted by telephone, face-to-face interviewing, having become expensive and dangerous to accomplish in most urban areas of the continent [2]. Outside North America, face-to-face interviews are still done, although costs are high and there are threats to the safety of interviewers. Computer-assisted telephone (CATI) survey is the main method presently in some countries, especially in North America. However, the overall response rate is low. In order to overcome the shortfall in trips of CATI [3, 4], some countries, such as the US and Switzerland, try to have a household traffic surveys using GPS location devices [59]. However, it also faces some problems. For example, high expense: GPS devices are fairly expensive, with passive devices, capable of storing many days’ worth of data, costing on the order of US$750 each; signal loss: serious degradation of the signal often happens in various circumstances, including tunnels, urban canyons, and heavy tree canopies, and in certain types of vehicles.

The past 20 years has seen a tremendous increase in internet use and computer-mediated communication. Researches of online populations have led to an increase in the use of online surveys [1013]. On one hand, it has some advantages including access to individuals in distant locations, the ability to reach difficult to contact participants, and the convenience of having automated data collection, which reduces researcher time and effort [14]. On the other hand, the disadvantages, such as uncertainty over the validity of the data and sampling issues, and concerns surrounding the design, implementation, and evaluation of an online survey are also obvious [14].

In order to cast off the deficiency of one kind traffic survey method, several traffic survey methods are used to reap the accurate traffic data in Qingdao’s third traffic survey (2016). They include online-survey, face-to-face interviewing, public transport survey, traffic flow survey, exit-entry survey, and other surveys (see Section 2).

Travel behavior study often needs huge traffic data which would include traffic survey data (such as household survey data) and some other data (such as mobile phone data, and GPS data) which is related to travel behavior that is also required. A number of studies analyze the details of travel behavior using household traffic survey data [1527]. Revealing general characteristics of travel behavior, the researchers also use the household traffic survey data to achieve a variety of particular research purposes [2836].

Some researchers have done some travel behavior analysis using the fusion data or multisource traffic data, such as the smart card data [3744]; location-based services data (Global Positioning System (GPS) devices [4548]). With respect to mobile phone systems, we can see Steenbruggen, Borzacchiello, Nijkamp, and Scholten, 2013 for a review.

Considering the disadvantages of household traffic survey data, we analyzed the travel behavior with different purposes and travel modes (car and bus) by combining household traffic survey data in 2016 and Baidu electric map API data in this paper. The accuracy of Baidu electric map API data is also validated by the actual taxi OD pair survey. This paper is organized as follows. The detail of Qingdao’s 2016 traffic survey is described in Section 2, and the Baidu electric map API data in our research are presented in Section 3. In Section 4, the results of trip time and distance with different purposes and modes are shown and analyzed in detail. Finally, we discuss our work in Section 5.

2. Household Traffic Survey Data in Qingdao

2.1. Necessity of the Third Traffic Survey

Qingdao had two household traffic surveys in 2002 and 2010, respectively. The first survey in 2002 found out the basic rule of the residents’ travel at that time and had an important effect on traffic network analysis. In order to adapt the city’s rapid development and the needs of subway construction, the government had the second traffic survey in 2010, which plays an important foundation supporting role in the design of subway lines 1, 2, 3, 4, and 8. After that, great changes have taken places in recent years in Qingdao city’s population quantity, structure, function, area, population structure, occupational structure of residents, trip structure, and the car ownership and the original survey data cannot support the resident trip analysis now. In view of this, the government had the third traffic survey in 2016. Figures 1 and 2 give the average trips, average trip time, and average distance from 1992 to 2015 and Figure 3 shows the urban area expansion from 1992 to 2015.

2.2. Traffic Survey Design

(1) Preliminary Preparation for the Third Traffic Survey. In order to improve the enthusiasm for participation and ensure the smooth implementation of the trip survey, the sense of this household traffic survey and the method of filling in questionnaires of resident trip survey are propagated by television news, newspaper, broadcasting, bus TV, Metro TV, and so on. Some Wechat public accounts, such as Qingdao Daily, Qingdao News, and New Life of Qingdao Metro, are also used to push the sense of this household traffic survey and the online-survey questionnaires.

(2) Traffic Survey Design. The third survey is classified into 6 categories and 16 classifications, which includes household survey, public transport survey, traffic flow survey, exit-entry survey, mobile phone data, and other surveys. The specific survey content is listed in Table 1 and the main survey point is shown in Figure 4. As shown in Table 1, this survey combines mobile phone data, traffic detection data, online-survey data, face-to-face interviewing data, IC card data, and GPS data. Resident trip survey mainly adopted online-survey questionnaire and household survey questionnaire. Public transport survey includes subway survey, bus car data, GPS data, and taxi data. Traffic flow survey and exit and entry survey mainly used the traffic surveillance system and traffic flow monitoring system.


6 categories16 classificationSurvey timeScope and source of investigation

Mobile phone dataMobile phone dataAug-Oct,2015All citizens in Qingdao

Household surveyonline-survey/Apl-,201638740 families in Qingdao
Face-to-face/Apl,20162500 questionnaires

Public transport surveyRail traffic survey/Apl-/Apl,201610 stations of M3
Bus survey/Sep-/Sep,2015Bus card data of 334 lines, one week’s GPS data of 5387 buses
Taxi survey/Apl-/Apl,2015All taxis in Qingdao

Traffic flow surveyCheck line survey/Apl-/Apl,201670 roads of 5 check lines
Important intersections/Apl-/Apl,2016293 intersections
Speed survey/Sep-/Sep,2015One week’s GPS data of buses

Exit-Entry surveyTraffic volume survey of external passenger hubDec,2015Airport, Railway stations and Coach stations
Volume survey of highwaysSep,2015All highways of Qingdao
Volume survey of expressway entrancesSep,2015Expressway entrances in Qingdao

Other surveysLand use statusSep-Dec,2015Urban and Rural Planning Bureau
Population-employmentSep-Dec,2015Statistical Bureau
Hotel distributionSep-Dec,2015Tourism Bureau
Car ownership distributionSep-Dec,2015Traffic Police detachment

As shown in Table 2, every row is a trip (OD). Family 3 has 3 members, i.e., 8, 9, and 10. Family 4 has 2 members, i.e., 11 and 12.


OD NumberFamily IDPersonal IDOriginDestinationPurposeTrip mode
LongitudeLatitudeDeparture timeLongitudeLatitudeArrival timeBusCarTaxiWalkSubwayBikeCommuting Bus

1238120.437836.10077:30120.414436.13147:55WorkNoYesNoNoNoNoNo
1338120.414436.085717:30120.379636.087517:45Shopping or F&BNoYesNoNoNoNoNo
1438120.379636.087518:15120.437836.100718:30HomeNoYesNoNoNoNoNo
1539120.437836.10077:20120.437736.10087:25SchoolNoNoNoYesNoNoNo
1639120.437736.100816:00120.437836.100716:05HomeNoNoNoYesNoNoNo
14310120.437836.10077:30120.433536.12257:50WorkYesNoNoNoNoNoNo
15310120.433536.122517:00120.437836.100717:40HomeYesNoNoNoNoNoNo
16411120.398836.08137:10120.411836.12078:10WorkYesNoNoNoNoNoNo
17411120.411836.120718:00120.398836.081319:10HomeYesNoNoNoNoNoNo
18412120.398836.08137:10120.397636. 10277:56WorkNoNoNoNoYesNoNo
19412120.397636.102720:00120.398836.081320:40HomeNoNoNoNoYesNoNo

3. Electric Map API Data

Although traditional traffic survey could reap some data we need, most traffic survey, especially the online-survey and face-to-face interviewing only acquire the OD information, trip mode, and trip time. It is difficult to get the trip routes information. At the same time, the trip time is usually inaccurate. What is exciting is that electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, Auto Navi Maps and so on) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. In this paper, we try to use Baidu Maps to find the routes.

Baidu Map supplies Web API v2.0 service for developers. People could obtain the route planning service by the style of HTTP/HTTPS. Table 3 shows the data sample from Baidu Maps Web API v2.0.


OD
Number
Recommended Travel RoutesLength (m)Travel time(s)Initial Walk Distance (m)Initial walk time (s)Travel distance by busTravel time by busArrival walk distance (m)Arrival walk time (s)

81106853286164131102022300319255
82105803782383306229183255204
831236341933863081089024151087869
8411989414517914310447231113631090
85111834127143114363290323258

As shown in Table 3, for one OD, it recommends 5 routes. For bus travelers, it includes the route length, travel time, initial walk time, initial walk time, travel distance by bus, travel time by bus, arrival walk distance, and arrival walk time and so on.

In fact, we do not know the data validity of the Baidu Maps Web API v2.0. For this, we take taxi as the test object and have a taxi follow investigation. The accuracy of the Baidu Maps Web API v2.0 is checked by comparing the recommended data and actual taxi investigate data.

Table 4 gives the style of taxi following questionnaire. As shown in Table 4, taxi following questionnaire includes OD pairs, departure time, arrival time, the passing intersections, and the road and traffic conditions.


Taxi following questionnaire

Weather:—Date: —Investigator: —

OD NumberDeparture timeOriginPassing intersectionsDestinationArrival timeTravel time cost(m)Road and traffic conditions
12

18:00Railway StationTaian Road-Feixian RoadFeixian Road-Feixian branch RoadPolar Ocean World8:5252Congestion

29:30MIXCHongkong Road-Shandong RoadShandong Road-Minjiang RoadShiyan community10:0232Accident

313:30TaidongTaidong 6th Road-Weihai RoadChenkou Road-Weihai RoadJinguihuayuan community13:5323Normal

414:23Wanda PlazaXuzhou Road-Longcheng RoadXuzhou Road-Dunhua RoadFuan community14:4825Normal

516:20No.59 middle schoolYanan 3th Road-Huayan RoadYanan 3th Road-Xiuzhan 3th RoadLiantong Building16:288Normal

617:00Tianjiahuayuan communityXintian Road-Hetian RoadXintan Road-Ningxia RoadNo.44 middle school17:1818Congestion

718:00No.83, Jiangxi RoadJiangxi Road-Panyanghu RoadJiangxi Road-Nanjing RoadNo.162, Jiaoning Road18:1111Congestion

820:20No.1517, Wutaishan RoadWutaishan Road-Tuanjie RoadTuanjie Road-Jialingjiang west RoadTianjiameiju community19:0040Normal

Here, we randomly chose 300 OD pairs in Qingdao city from the total taxi survey ODs. Figure 5 shows the recommendation routes of the 300 OD pairs generated by Baidu Maps Web API v2.0.

3.1. Accuracy Analysis on Recommended Routes Generated by Baidu Maps Web API v2.0

The coincidence factor of intersections is used to verify the accuracy of Web API. Specifically, the th OD pair’s matching rate can be written as follows:In (1), is the th OD pair’s matching rate. When is bigger, the accuracy of the Web API data will be higher. is the coincidence number of intersections between the Web API’s recommendation route and actual taxi survey route of the th OD pair. is the number of intersections of actual taxi survey route of the th OD pair. is the number of OD pairs. Accordingly, the average matching rate of total OD pairs can be written as follows:where is the total number of survey OD pairs.

According to statistical analysis, the average matching rate of total OD pairs is 90.74%. It is a high ratio which reflects high accuracy of Web API v2.0. However, in statistics, not all OD pairs’ matching rates are high. Here we chose three kinds of OD to analyze the matching rates. Table 5 gives the three kinds of OD pairs.


SameRecommendationSurveyRecommendationSurvey

OriginMIXC Taidong Wanda Plaza

Destination Shiyan community Jinguihuayuan community Fuan community

IntersectionsHongkong Road-Shandong RoadTaidong 6th Road-Weihai RoadTaidong 6th Road-Weihai RoadYanji Road-Xuzhou RoadXuzhou Road-Longcheng Road
Shandong Road-Minjiang RoadChenkou Road-Weihai RoadChenkou Road-Weihai RoadYanji Road-Lianyungang RoadXuzhou Road-Dunhua Road
Shandong Road-Jiangxi RoadChangchun Road-Weihai RoadChangchun Road-Weihai RoadYanji Road-Nanjing RoadDunhua Road-Lianyungang Road
Shandong Road-Jiaoning RoadHankou Road-Weihai RoadHankou Road-Weihai RoadYanji Road-Wuxing RoadLiangyungang Road-Haizhou Road
Shandong Road-Yanji RoadShandong Road-Hangan RoadHankou Road-Hexing RoadYanji Road-Shaoxing RoadLianyungang Road-Hangan Road
Shandong Road-Anshan RoadAnshan Road- Harbin RoadDunhua Road-Zhenjiang RoadYanji Road-Qingtian RoadFuzhoubei Road-Dunhua Road
Shandong Road-Fushun Road-Xiwu Road-Zhenjiang RoadQingting Road-Dunhua RoadDunhua Road-Yongji Road
Hailun Road-Anda Road-Shandong Road-Hangan RoadDunhua Road-Yongji Road-
--Anshan Road- Harbin Road--

Matching Rate100%66.7%14.3%

As shown in Table 5, between OD pair MIXC and Shiyan community, taxi survey route and Baidu’s recommended route have the same passing intersections. The ratio of intersections overlap is 100%, which reflects the Baidu API’s high accuracy. However, the OD pair Taidong and Jinguihuayuan community has the different results. There are three different intersections. The matching rate is 66.7%. The reason is explicable. We found that it was in evening peak, and the traffic congestion made the driver changing the route. Surprisingly, the survey route and recommendation route are totally non-overlapping between OD pair Wanda Plaza and Fuan community. The matching rate is only 14.3%. According to the investigation on Yanji Road, it is in construction repair during the survey day. Therefore, the taxi driver changed the trip route.

In a word, although the matching rates of intersections between some OD pairs are low, they are imaginable. Some unforeseen circumstances, such as the bad weather, accident, and congestion, would make drivers change their route which deviates from recommended routes.

Undoubtedly, according to the statistics on the 300 OD pairs, OD pairs’ matching rates are high (90.74%). Therefore, it is highly accurate and credible.

3.2. Accuracy Analysis on Recommended Trip Time Generated by Baidu Maps Web API v2.0

In order to further verify the validity of Baidu map Web API data, we also compare the travelling time. Here, two kinds of data are used to verify the accuracy of recommended trip time generated by Baidu Maps Web API v2.0. One is the 300 taxi OD survey data. As shown in Table 4, the trip time is also recorded. Here the ratio between the recommendation trip time and actual taxi survey time is used to verify the accuracy of the Baidu electric map API data. Specifically, the th OD pair’s time matching rate can be written as follows: where is the Baidu electric map API recommendation trip time of the th OD pair. denotes the actual taxi survey trip time of the th OD pair. The average time ratio of total OD pairs can be calculated as follows:

According to statistical analysis, the average ratio of total OD pairs is 78.16%, which is lower than the average route matching rate of total OD pairs (90.74%). Unlike trip route, trip time has higher randomness, even the same route, the same time of different days, or the same route, different time with the same day. Therefore, we think the ratio (78.16%) is high enough that could reflect the high accuracy of Baidu electric map API data.

4. Data Fusion and Traveler Behavior Analysis in Time and Space

4.1. Data Fusion, Processing, and Curve Fitting

Trip survey could obtain the accurate OD pairs, trip mode, travel purpose and so on. However, it cannot reap the travel route information and the travel time is inaccurate. Consider that Web API could provide route planning service and its high accuracy. This paper tries to take advantage of the two kinds of data and integrate them together and then have some traffic behavior analysis based on the integrated data. Table 6 gives a sample of the integrated data. Table 6 includes the OD pairs, the length of the route between one OD, the origin coordinate, the destination coordinate, the passing intersections, trip purpose, trip mode, walk distance, and walk time.


OD NumberLength (m)Travel time
(s)
OriginPassed intersectionsDestinationPurposeWalking distance(m)Walking time (s)Trip mode
LongitudeLatitudeLongitudeLatitude

1263192368120.437836.1007120.414436.1314Work1036600Bus
1396343046120.437836.0178120.379636.0875Shopping or F&B759450Bus
14141423531120.379636.0875120.437836.1007Home12980Car
1561152356120.437836.1007120.437836.1008Home926560bus
1631641261120.437736.0813120.411836.1207Work826600bus
171841600120.411836.1207120.398836.0813Home527506bus
182436760120.398836.0813120.397636. 1027Work679620bus
1943261642120.397636.1027120.398836.0813Home260206car

Matplotlib is used to have a python drawing. Specifically, Pylab and pyplot are the main processing tools. Hist function in pyplot is used to have interval setting and sample statistics of corresponding intervals. Here, data is set 150 intervals by considering the numerical span and frequency statistics. According to every interval’s median value (independent variable) and the statistics (dependent variable), the least-square method is used to curve fitting. The mean and standard deviation of the travel time and distance are analyzed by Pandas in Python.

4.2. Travel Behavior Analysis with Different Purposes

This section discussed the travel behavior with 6 purposes (i.e., work, home, school, taking children to school, shopping, and company business) from the angle of trip distance and time.

Figure 6 shows trip distance distribution with 6 purposes. As shown in Figure 6, all curves with different trip purposes show normal distribution approximately. Most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is relevant to the urban size, residence distribution, company distribution, and school distribution. Figure 7 shows the distribution of schools, residential communities, companies, supermarkets, and shopping arcades of Qingdao city. From Figure 7, we can see that, unlike western developed countries, Qingdao’s schools, residential communities, companies, supermarkets, and shopping arcades are concentrated in the urban area, which reflects Chinese most cities’ characteristics of non-separation of work and residence. It is worth noting that students have the shortest travel distance. The average travel distance is 5.34 kilometers. Similarly, taking children to school is the second shortest travel distance. Their average travel distance is 5.98 kilometers. It may have three reasons: most families preferring to choose nearby schools if their children go to school alone; the Qingdao’s education policy (such as the nearby enrollment policy); the number of schools being large.

For company business, its travel distance distribution is dispersed, and it has the longest travel distance (13.37 kilometers) which may be related to the randomness of company business.

As shown in Figure 8, the same with travel distance distribution, the average travel time of going to school and taking children to school is minimum. They are 22.54 minutes and 19.44 minutes. Because some parents drive children to school, its average travelling time is less than children going to school themselves. However, not all travelling time is directly proportional to travel distance. Taking company business and work as an example, although company business has the longest travel distance, work has the longest travel time instead of company business. Compared to travel distance, the standard deviations of all purposes’ travel time are bigger than the travel distance. This may be decided by the randomness of travel time. Unlike travel route, travel time is easier influenced by road and traffic conditions, such as signal control, traffic congestion, weather, accident, and driving habits. Another interesting thing is that most trip purposes have two peaks when the travel time is approximately 10 minutes and 30 minutes.

4.3. Travel Distance and Time Analysis with Different Modes
4.3.1. Travel Distance Analysis with Bus and Car

Figures 9, 10, and 11 show the travel distance distribution by bus and car with different purposes. For bus travelers, all purposes’ travel distance peaks are less than 5km and the average travel distance is 7.66km. Compared to work, going home and company business, going to school, taking children to school, and shopping’s travel distance distribution are more centralized. Their average travel distance is about 6.4km. The company business still has the longest average travel distance, 9.51km. Compared to bus travelers, car users have longer travel distance; their average travel distance is 8.58km. Work, going home, shopping, and company business travel by car have longer travel distance than bus. The most respective example is company business travel, 13.65km, 5 kilometers more than bus travel, which is determined by the high convenience and comfort of car service.

Interestingly, for purposes of going to school and taking children to school, car travelers will take shorter distance than bus travelers. The average travel distance of going to school by car is only 4.82km and taking children to school is 5.93km. Correspondingly, the average travel distance of going to school and taking children to school is 6.08km and 6.18km, respectively. Actually it is comprehensible. Car could achieve door-to-door service but not bus. Bus traveler should go to bus station first and then travel by bus. Some bus lines are even not the optimal route from home to school. However, for car traveler, they would choose the optimal route generally. In addition, bus travelers also must go to school from bus station. Thus, compared to car users, bus travelers would take more travel distance.

4.3.2. Travel Time Analysis with Bus and Car

Figures 12, 13, and 14 present car and bus travel time distribution with different purposes. Unlike travel distance distribution, travel time distribution has a great difference between car and bus. Bus travelers would spend more time than car travelers when finish their travel. Taking work as the example, compared to car users, bus traveler spends more than 28 minutes on average. On one hand, it is determined by the characteristic of bus. Bus travelers take extra walking time (go to station and go to work place), station waiting time, bus stop time, and even transfer time. On the other hand, it also reflects the low efficiency of bus system, such as the low departure frequency, unreasonable bus line and station setting, no bus lanes, and so on. The big difference between car and bus in travel time would induce more car travel demand which increases traffic congestion.

Therefore, in order to alleviate the congestion, the traffic manager should improve the bus service to suppress the high increasing of the car demand. As shown in Figure 13, most bus travelers’ travel durations are between the intervals of 20 and 60 minutes. Most car travelers’ travel durations are between the intervals 2 and 40 minutes.

5. Discussion

This paper tries to integrate resident trip survey data and electric map API data together. In order to verify the accuracy of electric map API data, we take taxi as the test object and have a taxi follow investigation. Accuracy of recommended routes and travel time is analyzed. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. In statistics, not all OD pairs’ matching rates are high. Some unforeseen circumstances, such as the bad weather, accident, and congestion, would make drivers change their route which deviates from recommended routes. According to statistical analysis, the average ratio of total OD pairs is 78.16%, which is lower than the average route matching rate of total OD pairs (90.74%). Unlike trip route, trip time has higher randomness, even the same route, or the same time of different days, or the same route, different time with the same day. Therefore, we think the ratio (78.16%) is high enough that could reflect the high accuracy of electric map API data.

Based on the fusion data, travel behavior with different purposes and modes is analyzed. We found that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is relevant to the urban size, residence distribution, company distribution, and school distribution. Unlike western developed countries, Qingdao’s schools, residential communities, companies, supermarkets, and shopping arcades are concentrated in the urban area, which reflects Chinese most cities’ characteristics of non-separation of work and residence. It is worth noting that students have the shortest travel distance. The average travel distance is 5.34 kilometers. Company business’s travel distance distribution is dispersed, and it has the longest travel distance (13.37 kilometers) which may be related to the randomness of company business. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. This may be decided by the randomness of travel time. Unlike travel route, travel time is easier influenced by road and traffic conditions, such as signal control, traffic congestion, weather, accident, driving habits, and so on.

For bus travelers, all purposes’ travel distance peaks are less than 5km and the average travel distance is 7.66km. Compared to work, going home and company business, going to school, taking children to school, and shopping’s travel distance distribution are more centralized. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km. Work, going home, shopping, and company business travel by car have longer travel distance than bus. The most respective example is company business travel, 13.65km, 5 kilometers more than bus travel, which is determined by the high convenience and comfort of car service. What is surprising is that, for purposes of going to school and taking children to school, car travelers will take shorter distance than bus travelers.

Unlike travel distance distribution, travel time distribution has a great difference between car and bus. Bus travelers would spend more time than car travelers when finish their travel. The big difference between car and bus in travel time would induce more car travel demand which increases traffic congestion. Therefore, in order to alleviate the congestion, the traffic manager should improve the bus service to suppress the high increasing of the car demand.

Data Availability

The survey data are put into the file of Supplementary Materials. The route information of Baidu recommend is available online at https://map.baidu.com/.

Disclosure

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This paper is partly supported by the NSFC (71801144, 71371111, 71322102, and 71701189) and the China National Funds for Distinguished Young Scientists (71525002).

Supplementary Materials

Attachment 1 is the field survey data and Baidu recommend routes of 300 taxi OD pairs. Attachment 2 is the OD pairs information with bus. Attachment 3 is the OD pairs information with car. (Supplementary Materials)

References

  1. P. R. Stopher and S. P. Greaves, “Household traffic surveys: Where are we going?” Transportation Research Part A: Policy and Practice, vol. 41, pp. 367–381, 2007. View at: Publisher Site | Google Scholar
  2. P. R. Stopher and H. M. A. Metcalf, Methods for Household Traffic surveys, NCHRP Synthesis 236, Transportation Research Board, Washington, DC, USA, 1996.
  3. J. Wolf, M. Loechl, M. Thompson, and C. Arce, “Trip rate analysis in GPS-enhanced personal traffic surveys,” in Transport Survey Quality and Innovation, P. Stopher and P. Jones, Eds., pp. 483–498, Pergamon Press, 2003. View at: Google Scholar
  4. D. Pearson, A comparison of trip determination methods in GPS-enhanced household traffic surveys, paper to be presented to the 84th Annual Meeting of the Transportation Research Board, January 2005, 2004.
  5. J. Wolf, Defining GPS and Its Capabilities, D. A. Hensher, K. J. Button, and K. E. Haynes, Eds., 2004.
  6. P. R. Stopher, GPS, Location, and Household Travel, D. A. Hensher and K. J. Button, Eds., 2004.
  7. M. Kang, A. V. Moudon, P. M. Hurvitz, and B. E. Saelens, “Capturing fine-scale travel behaviors: a comparative analysis between personal activity location measurement system (PALMS) and travel diary,” International Journal of Health Geographics, vol. 17, no. 1, p. 40, 2018. View at: Google Scholar
  8. J. Y. Scully, A. V. Moudon, P. M. Hurvitz, A. Aggarwal, and A. Drewnowski, “GPS or travel diary: Comparing spatial and temporal characteristics of visits to fast food restaurants and supermarkets,” PLoS ONE, vol. 12, no. 4, Article ID e0174859, 2017. View at: Google Scholar
  9. A. Neven, I. D. Schutter, G. Wets, P. Feys, and D. Janssens, “Data quality of travel behavior studies: factors influencing the reporting rate of self-reported and gps-recorded trips in persons with disabilities,” Transportation Research Record, 2018. View at: Google Scholar
  10. G. Terhanian, J. Bremer, J. Olmsted, and J. Guo, “A process for developing an optimal model for reducing bias in nonprobability samples,” Journal of Advertising Research, vol. 56, no. 1, pp. 14–24, 2016. View at: Publisher Site | Google Scholar
  11. Statista., Statista, “Dossier: market research”, https://goo.gl/A2e6hb, 2017.
  12. Statista, “Dossier: mobile search”, https://goo.gl/e8prXU, 2017.
  13. J. R. Evans and A. Mathur, “The value of online surveys: a look back and a look ahead,” Internet Research, vol. 28, no. 4, pp. 854–887, 2018. View at: Publisher Site | Google Scholar
  14. K. B. Wright, “Researching internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services,” Journal of Computer-Mediated Communication, vol. 10, no. 3, 2005. View at: Google Scholar
  15. F. M. Dieleman, M. Dijst, and G. Burghouwt, “Urban form and travel behaviour: micro-level household attributes and residential context,” Urban Studies, vol. 39, no. 3, pp. 507–527, 2002. View at: Publisher Site | Google Scholar
  16. S. Lee and S. Lee, “Transport policy directions based on travel pattern analysis in Seoul Metropolitan Area,” International Journal of Urban Sciences, vol. 9, pp. 40–48, 2005. View at: Publisher Site | Google Scholar
  17. G. Giuliano and J. Dargay, “Car ownership, travel and land use: A comparison of the US and Great Britain,” Transportation Research Part A: Policy and Practice, vol. 40, no. 2, pp. 106–124, 2006. View at: Publisher Site | Google Scholar
  18. S. H. Choo, S. N. Kwon, and D. H. Kim, “Exploring characteristics on trip chaining: the case of Seoul,” Journal of Korean Transportation Society, vol. 26, pp. 87–97, 2008 (Korean). View at: Google Scholar
  19. C. Chen, J. Chen, and J. Barry, “Diurnal pattern of transit ridership: a case study of the New York City subway system,” Journal of Transport Geography, vol. 17, no. 3, pp. 176–186, 2009. View at: Publisher Site | Google Scholar
  20. D. Merom, H. P. van der Ploeg, G. Corpuz, and A. E. Bauman, “Public health perspectives on household traffic surveys: active travel between 1997 and 2007,” American Journal of Preventive Medicine, vol. 39, pp. 113–121, 2010. View at: Publisher Site | Google Scholar
  21. C. C. Cheong and R. Toh, Household Interview Surveys from 1997 to 2008: A Decade of Changing Travel Behaviours, LTA Academy, Land Transport Authority, Singapore, 2010.
  22. P. Stopher, Y. Zhang, J. Armoogum, and J.-L. Madre, “National household traffic surveys: the case for australia,” in Proceedings of the 34th Australasian Transport Research Forum (ATRF), Adelaide, Australia, 2011. View at: Google Scholar
  23. J. Pucher, R. Buehler, D. Merom, and A. Bauman, “Walking and cycling in the United States, 2001–2009: evidence from the National Household Traffic surveys,” American Journal of Public Health, vol. 101, pp. S310–S317, 2011. View at: Publisher Site | Google Scholar
  24. E. Shay and A. J. Khattak, “Household travel decision chains: Residential environment, automobile ownership, trips and mode choice,” International Journal of Sustainable Transportation, vol. 6, no. 2, pp. 88–110, 2012. View at: Publisher Site | Google Scholar
  25. W. D. Lee, Y. G. Na, S. H. Park, B. J. Lee, and C. H. Joh, “Analysis of transportation equity,” Journal of Korean Society of Urban Geographers, vol. 15, pp. 75–88, 2012 (Korean). View at: Google Scholar
  26. K. K. Taniguchi, Work Trips on Public Transportation: An Analysis of Trends, Select Markets, and Users Using the National Household Traffic survey Series, Diss. University of South Florida, 2012.
  27. J. Choi, W. D. Lee, W. H. Park, C. Kim, K. Choi, and C. H. Joh, “Analyzing changes in travel behavior in time and space using household traffic surveys in Seoul Metropolitan Area over eight years,” Travel Behaviour and Society, vol. 1, pp. 3–14, 2014. View at: Google Scholar
  28. G. Corpuz and J. Peachman, “Measuring the impacts of internet usage on travel behaviour in the sydney household traffic survey,” in Proceedings of the Paper Presented at the Australasia Transport Research Forum Conference, Wellington, New Zealand, 2003. View at: Google Scholar
  29. N. McDonald and M. Trowbridge, “Does the built environment affect when American teens become drivers? Evidence from the 2001 National Household Traffic survey,” Journal of Safety Research, vol. 40, no. 3, pp. 177–183, 2009. View at: Publisher Site | Google Scholar
  30. D. Brownstone and T. F. Golob, “The impact of residential density on vehicle usage and energy consumption,” Journal of Urban Economics, vol. 65, no. 1, pp. 91–98, 2009. View at: Publisher Site | Google Scholar
  31. M. C. Ouimet, B. G. Simons-Morton, P. L. Zador et al., “Using the U.S. National Household Traffic survey to estimate the impact of passenger characteristics on young drivers’ relative risk of fatal crash involvement,” Accident Analysis & Prevention, vol. 42, no. 2, pp. 689–694, 2010. View at: Publisher Site | Google Scholar
  32. G. Tal and S. Handy, “Travel behavior of immigrants: an analysis of the 2001 national household transportation survey,” Transport Policy, vol. 17, no. 2, pp. 85–93, 2010. View at: Publisher Site | Google Scholar
  33. J. McSaveney and L. Povey, “Alcohol and travel in the New Zealand Household Traffic survey,” in Proceedings of the Paper presented at the 33rd Australasian Transport Research Forum (ATRF), ACT, Canberra, Australia, October 2010. View at: Google Scholar
  34. R. Van Haaren, Assessment of Electric Cars‘ Range Requirements and Usage Patterns based on Driving Behavior recorded in the National Household Traffic survey of 2009, Earth and Environmental Engineering Department, Columbia University, Fu Foundation School of Engineering and Applied Science, New York, NY, USA, 2011.
  35. S. H. Choo, H. S. Lee, and H. J. Shin, “Analysis of change in elderly travel behavior using household traffic survey in Seoul Metropolitan Area,” Journal of Korea Institute for Human Settlements, vol. 76, pp. 31–45, 2013 (Korean). View at: Google Scholar
  36. J. Feng, M. Dijst, B. Wissink, and J. Prillwitz, “The impacts of household structure on the travel behaviour of seniors and young parents in China,” Journal of Transport Geography, vol. 30, pp. 117–126, 2013. View at: Publisher Site | Google Scholar
  37. P. T. Blythe, “Improving public transport ticketing through smart cards,” Proceedings of the Institution of Civil Engineers—Municipal Engineer, vol. 157, no. 1, pp. 47–54, 2004. View at: Publisher Site | Google Scholar
  38. T. Zhou, C. Zhai, and Z. Gao, “Approaching bus OD matrices based on data reduced from bus IC cards,” Urban Transport of China, vol. 5, no. 3, pp. 48–52, 2007 (Chinese). View at: Google Scholar
  39. C-H. Joh and C. Hwang, “Atime-geographic analysis of trip trajectories and land use characteristics in Seoul metropolitan area by using multidimensional sequence alignment and spatial analysis,” in Proceedings of the AAG 2010 Annual Meeting, Washington, DC, USA, 2010. View at: Google Scholar
  40. W. Jang, “Travel time and transfer analysis using transit smart card data,” Transportation Research Record, vol. 2144, pp. 142–149, 2010. View at: Google Scholar
  41. C. Roth, S. M. Kang, M. Batty, and M. Barthélemy, “Structure of urban movements: polycentric activity and entangled hierarchical flows,” PLoS ONE, vol. 6, no. 1, Article ID e15923, 2011. View at: Publisher Site | Google Scholar
  42. Y. Gong, Y. Liu, Y. Lin, J. Yang, Z. Duan, and G. Li, “Exploring spatiotemporal characteristics of intra-urban trips using metro smartcard records,” in Proceedings of the Geoinformatics, Hong Kong, 2012. View at: Google Scholar
  43. L. Sun, K. W. Axhausen, D.-H. Lee, and X. Huang, “Understanding metropolitan patterns of daily encounters,” Proceedings of the National Academy of Sciences, vol. 110, no. 34, pp. 13774–13779, 2013. View at: Publisher Site | Google Scholar
  44. Y. Long and J.-C. Thill, “Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing,” Computers, Environment and Urban Systems, vol. 53, pp. 19–35, 2015. View at: Publisher Site | Google Scholar
  45. F. Newhaus, “Urban diary – A tracking project,” UCL Working Paper Series, 151, 2009. View at: Google Scholar
  46. H. Gong, C. Chen, E. Bialostozky, and C. T. Lawson, “A GPS/GIS method for travel mode detection in New York City,” Computers, Environment and Urban Systems, vol. 36, no. 2, pp. 131–139, 2012. View at: Publisher Site | Google Scholar
  47. L. Liu, C. Andris, and C. Ratti, “Uncovering cabdrivers' behavior patterns from their digital traces,” Computers, Environment and Urban Systems, vol. 34, no. 6, pp. 541–548, 2010. View at: Publisher Site | Google Scholar
  48. Y. Yue, H. Wang, B. Hu, Q. Li, Y. Li, and A. G. O. Yeh, “Exploratory calibration of a spatial interaction model using taxi GPS trajectories,” Computers, Environment and Urban Systems, vol. 36, no. 2, pp. 140–153, 2012. View at: Publisher Site | Google Scholar

Copyright © 2019 Ge Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views1723
Downloads522
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.