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

Authors: Ge Gao; Zhen Wang; Xinmin Liu; Qing Li; Wei Wang; Junyou Zhang
Journal: Journal of Advanced Transportation
 (2019)

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## 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.


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## Body

## 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 [5–9]. However, it also faces some problems. For example, (1) 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; (2) 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 [10–13]. 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 Section2).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 [15–27]. Revealing general characteristics of travel behavior, the researchers also use the household traffic survey data to achieve a variety of particular research purposes [28–36].Some researchers have done some travel behavior analysis using the fusion data or multisource traffic data, such as the smart card data [37–44]; location-based services data (Global Positioning System (GPS) devices [45–48]). 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 Section2, 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. Figures1 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.Figure 1
Average trips per day in the 3 traffic surveys of Qingdao.Figure 2
Average trip time and distance per day in the 3 traffic surveys of Qingdao.Figure 3
Urban area change of Qingdao 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.Table 1
Traffic survey content.
6 categories 16 classification Survey time Scope and source of investigation Mobile phone data Mobile phone data Aug-Oct,2015 All citizens in Qingdao  Household survey online-survey 20 t h/Apl-21st,2016 38740 families in Qingdao Face-to-face 23 t h/Apl,2016 2500 questionnaires  Public transport survey Rail traffic survey 18 t h/Apl-26th/Apl,2016 10 stations of M3 Bus survey 14 t h/Sep-20th/Sep,2015 Bus card data of 334 lines, one week’s GPS data of 5387 buses Taxi survey 10 t h/Apl-16th/Apl,2015 All taxis in Qingdao  Traffic flow survey Check line survey 10 t h/Apl-16th/Apl,2016 70 roads of 5 check lines Important intersections 10 t h/Apl-16th/Apl,2016 293 intersections Speed survey 14 t h/Sep-20th/Sep,2015 One week’s GPS data of buses  Exit-Entry survey Traffic volume survey of external passenger hub Dec,2015 Airport, Railway stations and Coach stations Volume survey of highways Sep,2015 All highways of Qingdao Volume survey of expressway entrances Sep,2015 Expressway entrances in Qingdao  Other surveys Land use status Sep-Dec,2015 Urban and Rural Planning Bureau Population-employment Sep-Dec,2015 Statistical Bureau Hotel distribution Sep-Dec,2015 Tourism Bureau Car ownership distribution Sep-Dec,2015 Traffic Police detachmentFigure 4
The main traffic survey point.As shown in Table2, 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.Table 2
Online-survey samples.
OD Number Family ID Personal ID Origin Destination Purpose Trip mode Longitude Latitude Departure time Longitude Latitude Arrival time Bus Car Taxi Walk Subway Bike Commuting Bus 12 3 8 120.4378 36.1007 7:30 120.4144 36.1314 7:55 Work No Yes No No No No No 13 3 8 120.4144 36.0857 17:30 120.3796 36.0875 17:45 Shopping or F&B No Yes No No No No No 14 3 8 120.3796 36.0875 18:15 120.4378 36.1007 18:30 Home No Yes No No No No No 15 3 9 120.4378 36.1007 7:20 120.4377 36.1008 7:25 School No No No Yes No No No 16 3 9 120.4377 36.1008 16:00 120.4378 36.1007 16:05 Home No No No Yes No No No 14 3 10 120.4378 36.1007 7:30 120.4335 36.1225 7:50 Work Yes No No No No No No 15 3 10 120.4335 36.1225 17:00 120.4378 36.1007 17:40 Home Yes No No No No No No 16 4 11 120.3988 36.0813 7:10 120.4118 36.1207 8:10 Work Yes No No No No No No 17 4 11 120.4118 36.1207 18:00 120.3988 36.0813 19:10 Home Yes No No No No No No 18 4 12 120.3988 36.0813 7:10 120.3976 36. 1027 7:56 Work No No No No Yes No No 19 4 12 120.3976 36.1027 20:00 120.3988 36.0813 20:40 Home No No No No Yes No No


## 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. Figures1 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.Figure 1
Average trips per day in the 3 traffic surveys of Qingdao.Figure 2
Average trip time and distance per day in the 3 traffic surveys of Qingdao.Figure 3
Urban area change of Qingdao 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.Table 1
Traffic survey content.
6 categories 16 classification Survey time Scope and source of investigation Mobile phone data Mobile phone data Aug-Oct,2015 All citizens in Qingdao  Household survey online-survey 20 t h/Apl-21st,2016 38740 families in Qingdao Face-to-face 23 t h/Apl,2016 2500 questionnaires  Public transport survey Rail traffic survey 18 t h/Apl-26th/Apl,2016 10 stations of M3 Bus survey 14 t h/Sep-20th/Sep,2015 Bus card data of 334 lines, one week’s GPS data of 5387 buses Taxi survey 10 t h/Apl-16th/Apl,2015 All taxis in Qingdao  Traffic flow survey Check line survey 10 t h/Apl-16th/Apl,2016 70 roads of 5 check lines Important intersections 10 t h/Apl-16th/Apl,2016 293 intersections Speed survey 14 t h/Sep-20th/Sep,2015 One week’s GPS data of buses  Exit-Entry survey Traffic volume survey of external passenger hub Dec,2015 Airport, Railway stations and Coach stations Volume survey of highways Sep,2015 All highways of Qingdao Volume survey of expressway entrances Sep,2015 Expressway entrances in Qingdao  Other surveys Land use status Sep-Dec,2015 Urban and Rural Planning Bureau Population-employment Sep-Dec,2015 Statistical Bureau Hotel distribution Sep-Dec,2015 Tourism Bureau Car ownership distribution Sep-Dec,2015 Traffic Police detachmentFigure 4
The main traffic survey point.As shown in Table2, 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.Table 2
Online-survey samples.
OD Number Family ID Personal ID Origin Destination Purpose Trip mode Longitude Latitude Departure time Longitude Latitude Arrival time Bus Car Taxi Walk Subway Bike Commuting Bus 12 3 8 120.4378 36.1007 7:30 120.4144 36.1314 7:55 Work No Yes No No No No No 13 3 8 120.4144 36.0857 17:30 120.3796 36.0875 17:45 Shopping or F&B No Yes No No No No No 14 3 8 120.3796 36.0875 18:15 120.4378 36.1007 18:30 Home No Yes No No No No No 15 3 9 120.4378 36.1007 7:20 120.4377 36.1008 7:25 School No No No Yes No No No 16 3 9 120.4377 36.1008 16:00 120.4378 36.1007 16:05 Home No No No Yes No No No 14 3 10 120.4378 36.1007 7:30 120.4335 36.1225 7:50 Work Yes No No No No No No 15 3 10 120.4335 36.1225 17:00 120.4378 36.1007 17:40 Home Yes No No No No No No 16 4 11 120.3988 36.0813 7:10 120.4118 36.1207 8:10 Work Yes No No No No No No 17 4 11 120.4118 36.1207 18:00 120.3988 36.0813 19:10 Home Yes No No No No No No 18 4 12 120.3988 36.0813 7:10 120.3976 36. 1027 7:56 Work No No No No Yes No No 19 4 12 120.3976 36.1027 20:00 120.3988 36.0813 20:40 Home No No No No Yes No No


## 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. Table3 shows the data sample from Baidu Maps Web API v2.0.Table 3
Data sample of Baidu Maps Web API v2.0.
ODNumber Recommended Travel Routes Length (m) Travel time(s) Initial Walk Distance (m) Initial walk time (s) Travel distance by bus Travel time by bus Arrival walk distance (m) Arrival walk time (s) 8 1 10685 3286 164 131 10202 2300 319 255 8 2 10580 3782 383 306 229 183 255 204 8 3 12363 4193 386 308 10890 2415 1087 869 8 4 11989 4145 179 143 10447 2311 1363 1090 8 5 11183 4127 143 114 363 290 323 258As shown in Table3, 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.Table4 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.Table 4
Taxi following questionnaire.
Taxi following questionnaire Weather:—Date: —Investigator: —  OD Number Departure time Origin Passing intersections Destination Arrival time Travel time cost(m) Road and traffic conditions 1 2 …  1 8:00 Railway Station Taian Road-Feixian Road Feixian Road-Feixian branch Road … Polar Ocean World 8:52 52 Congestion  2 9:30 MIXC Hongkong Road-Shandong Road Shandong Road-Minjiang Road … Shiyan community 10:02 32 Accident  3 13:30 Taidong Taidong 6th Road-Weihai Road Chenkou Road-Weihai Road … Jinguihuayuan community 13:53 23 Normal  4 14:23 Wanda Plaza Xuzhou Road-Longcheng Road Xuzhou Road-Dunhua Road … Fuan community 14:48 25 Normal  5 16:20 No.59 middle school Yanan 3th Road-Huayan Road Yanan 3th Road-Xiuzhan 3th Road … Liantong Building 16:28 8 Normal  6 17:00 Tianjiahuayuan community Xintian Road-Hetian Road Xintan Road-Ningxia Road … No.44 middle school 17:18 18 Congestion  7 18:00 No.83, Jiangxi Road Jiangxi Road-Panyanghu Road Jiangxi Road-Nanjing Road … No.162, Jiaoning Road 18:11 11 Congestion  8 20:20 No.1517, Wutaishan Road Wutaishan Road-Tuanjie Road Tuanjie Road-Jialingjiang west Road … Tianjiameiju community 19:00 40 NormalHere, we randomly chose 300 OD pairs in Qingdao city from the total taxi survey ODs. Figure5 shows the recommendation routes of the 300 OD pairs generated by Baidu Maps Web API v2.0.Figure 5
Recommendation routes of the 300 OD pairs.


### 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, theith OD pair’s matching rate can be written as follows:(1)ωi=NisNid,i=1,2,3,…,n;n∈Z+In (1), ωi is the ith OD pair’s matching rate. When ωi is bigger, the accuracy of the Web API data will be higher. Nis is the coincidence number of intersections between the Web API’s recommendation route and actual taxi survey route of the ith OD pair. Nid is the number of intersections of actual taxi survey route of the ith OD pair. n is the number of OD pairs. Accordingly, the average matching rate of total OD pairs can be written as follows:(2)ω=∑i=1nNis/Nidn,n∈Z+where n 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. Table5 gives the three kinds of OD pairs.Table 5
Matching rate of three kinds of OD pairs.
Same Recommendation Survey Recommendation Survey Origin MIXC Taidong Wanda Plaza  Destination Shiyan community Jinguihuayuan community Fuan community  Intersections Hongkong Road-Shandong Road Taidong 6th Road-Weihai Road Taidong 6th Road-Weihai Road Yanji Road-Xuzhou Road Xuzhou Road-Longcheng Road Shandong Road-Minjiang Road Chenkou Road-Weihai Road Chenkou Road-Weihai Road Yanji Road-Lianyungang Road Xuzhou Road-Dunhua Road Shandong Road-Jiangxi Road Changchun Road-Weihai Road Changchun Road-Weihai Road Yanji Road-Nanjing Road Dunhua Road-Lianyungang Road Shandong Road-Jiaoning Road Hankou Road-Weihai Road Hankou Road-Weihai Road Yanji Road-Wuxing Road Liangyungang Road-Haizhou Road Shandong Road-Yanji Road Shandong Road-Hangan Road Hankou Road-Hexing Road Yanji Road-Shaoxing Road Lianyungang Road-Hangan Road Shandong Road-Anshan Road Anshan Road- Harbin Road Dunhua Road-Zhenjiang Road Yanji Road-Qingtian Road Fuzhoubei Road-Dunhua Road Shandong Road-Fushun Road - Xiwu Road-Zhenjiang Road Qingting Road-Dunhua Road Dunhua Road-Yongji Road Hailun Road-Anda Road - Shandong Road-Hangan Road Dunhua Road-Yongji Road - - - Anshan Road- Harbin Road - -  Matching Rate 100% 66.7% 14.3%As shown in Table5, 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 Table4, 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 ith OD pair’s time matching rate can be written as follows: (3)θi=min⁡Tid,Tismax⁡Tid,Tis,i=1,2,3,…,n;n∈Z+where Tis is the Baidu electric map API recommendation trip time of the ith OD pair. Tid denotes the actual taxi survey trip time of the ith OD pair. The average time ratio of total OD pairs can be calculated as follows:(4)θ=∑i=1nmin⁡Tid,Tis/max⁡Tid,Tisn,n∈Z+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.


## 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, theith OD pair’s matching rate can be written as follows:(1)ωi=NisNid,i=1,2,3,…,n;n∈Z+In (1), ωi is the ith OD pair’s matching rate. When ωi is bigger, the accuracy of the Web API data will be higher. Nis is the coincidence number of intersections between the Web API’s recommendation route and actual taxi survey route of the ith OD pair. Nid is the number of intersections of actual taxi survey route of the ith OD pair. n is the number of OD pairs. Accordingly, the average matching rate of total OD pairs can be written as follows:(2)ω=∑i=1nNis/Nidn,n∈Z+where n 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. Table5 gives the three kinds of OD pairs.Table 5
Matching rate of three kinds of OD pairs.
Same Recommendation Survey Recommendation Survey Origin MIXC Taidong Wanda Plaza  Destination Shiyan community Jinguihuayuan community Fuan community  Intersections Hongkong Road-Shandong Road Taidong 6th Road-Weihai Road Taidong 6th Road-Weihai Road Yanji Road-Xuzhou Road Xuzhou Road-Longcheng Road Shandong Road-Minjiang Road Chenkou Road-Weihai Road Chenkou Road-Weihai Road Yanji Road-Lianyungang Road Xuzhou Road-Dunhua Road Shandong Road-Jiangxi Road Changchun Road-Weihai Road Changchun Road-Weihai Road Yanji Road-Nanjing Road Dunhua Road-Lianyungang Road Shandong Road-Jiaoning Road Hankou Road-Weihai Road Hankou Road-Weihai Road Yanji Road-Wuxing Road Liangyungang Road-Haizhou Road Shandong Road-Yanji Road Shandong Road-Hangan Road Hankou Road-Hexing Road Yanji Road-Shaoxing Road Lianyungang Road-Hangan Road Shandong Road-Anshan Road Anshan Road- Harbin Road Dunhua Road-Zhenjiang Road Yanji Road-Qingtian Road Fuzhoubei Road-Dunhua Road Shandong Road-Fushun Road - Xiwu Road-Zhenjiang Road Qingting Road-Dunhua Road Dunhua Road-Yongji Road Hailun Road-Anda Road - Shandong Road-Hangan Road Dunhua Road-Yongji Road - - - Anshan Road- Harbin Road - -  Matching Rate 100% 66.7% 14.3%As shown in Table5, 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 Table4, 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 ith OD pair’s time matching rate can be written as follows: (3)θi=min⁡Tid,Tismax⁡Tid,Tis,i=1,2,3,…,n;n∈Z+where Tis is the Baidu electric map API recommendation trip time of the ith OD pair. Tid denotes the actual taxi survey trip time of the ith OD pair. The average time ratio of total OD pairs can be calculated as follows:(4)θ=∑i=1nmin⁡Tid,Tis/max⁡Tid,Tisn,n∈Z+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. Table6 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.Table 6
Sample of the integrated data.
OD Number Length (m) Travel time (s) Origin Passed intersections Destination Purpose Walking distance(m) Walking time (s) Trip mode Longitude Latitude Longitude Latitude 12 6319 2368 120.4378 36.1007 … 120.4144 36.1314 Work 1036 600 Bus 13 9634 3046 120.4378 36.0178 … 120.3796 36.0875 Shopping or F&B 759 450 Bus 14 14142 3531 120.3796 36.0875 … 120.4378 36.1007 Home 129 80 Car 15 6115 2356 120.4378 36.1007 … 120.4378 36.1008 Home 926 560 bus 16 3164 1261 120.4377 36.0813 … 120.4118 36.1207 Work 826 600 bus 17 1841 600 120.4118 36.1207 … 120.3988 36.0813 Home 527 506 bus 18 2436 760 120.3988 36.0813 … 120.3976 36. 1027 Work 679 620 bus 19 4326 1642 120.3976 36.1027 … 120.3988 36.0813 Home 260 206 carMatplotlib 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.Figure6 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: (1) most families preferring to choose nearby schools if their children go to school alone; (2) the Qingdao’s education policy (such as the nearby enrollment policy); (3) the number of schools being large.Figure 6
Trip distance distribution with 6 travel purposes.Figure 7
Distributions of residential communities, schools, offices, and shopping centers in Qingdao.
(a)
The distribution of schools (b)
The distribution of offices (c)
The distribution of shopping arcadesFor 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 Figure8, 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.Figure 8
Trip time distribution with 6 travel purposes.


### 4.3. Travel Distance and Time Analysis with Different Modes

#### 4.3.1. Travel Distance Analysis with Bus and Car

Figures9, 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.Figure 9
Car and bus travel distance distribution with different purposes.Figure 10
Comparison of travel distance with different purpose by bus.Figure 11
Comparison of travel distance with different purpose by car.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

Figures12, 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.Figure 12
Car and bus travel time distribution with different purposes.Figure 13
Comparison of bus travel time with different purposes.Figure 14
Comparison of car travel time with different purposes.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 Figure13, 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.


## 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. Table6 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.Table 6
Sample of the integrated data.
OD Number Length (m) Travel time (s) Origin Passed intersections Destination Purpose Walking distance(m) Walking time (s) Trip mode Longitude Latitude Longitude Latitude 12 6319 2368 120.4378 36.1007 … 120.4144 36.1314 Work 1036 600 Bus 13 9634 3046 120.4378 36.0178 … 120.3796 36.0875 Shopping or F&B 759 450 Bus 14 14142 3531 120.3796 36.0875 … 120.4378 36.1007 Home 129 80 Car 15 6115 2356 120.4378 36.1007 … 120.4378 36.1008 Home 926 560 bus 16 3164 1261 120.4377 36.0813 … 120.4118 36.1207 Work 826 600 bus 17 1841 600 120.4118 36.1207 … 120.3988 36.0813 Home 527 506 bus 18 2436 760 120.3988 36.0813 … 120.3976 36. 1027 Work 679 620 bus 19 4326 1642 120.3976 36.1027 … 120.3988 36.0813 Home 260 206 carMatplotlib 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.Figure6 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: (1) most families preferring to choose nearby schools if their children go to school alone; (2) the Qingdao’s education policy (such as the nearby enrollment policy); (3) the number of schools being large.Figure 6
Trip distance distribution with 6 travel purposes.Figure 7
Distributions of residential communities, schools, offices, and shopping centers in Qingdao.
(a)
The distribution of schools (b)
The distribution of offices (c)
The distribution of shopping arcadesFor 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 Figure8, 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.Figure 8
Trip time distribution with 6 travel purposes.


## 4.3. Travel Distance and Time Analysis with Different Modes

### 4.3.1. Travel Distance Analysis with Bus and Car

Figures9, 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.Figure 9
Car and bus travel distance distribution with different purposes.Figure 10
Comparison of travel distance with different purpose by bus.Figure 11
Comparison of travel distance with different purpose by car.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

Figures12, 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.Figure 12
Car and bus travel time distribution with different purposes.Figure 13
Comparison of bus travel time with different purposes.Figure 14
Comparison of car travel time with different purposes.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 Figure13, 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.


## 4.3.1. Travel Distance Analysis with Bus and Car

Figures9, 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.Figure 9
Car and bus travel distance distribution with different purposes.Figure 10
Comparison of travel distance with different purpose by bus.Figure 11
Comparison of travel distance with different purpose by car.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

Figures12, 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.Figure 12
Car and bus travel time distribution with different purposes.Figure 13
Comparison of bus travel time with different purposes.Figure 14
Comparison of car travel time with different purposes.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 Figure13, 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.



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*Source: 6383097-2019-03-11.xml*