Trip mode split is the result of interrelated and mutually independent factors, such as city scale, urban form, economic level, trip distance, and travel time. In order to analyze the formation of traffic structure, it is necessary to make a comprehensive study on the mechanism of these factors and obtain the basic causal relationship of them. Based on this, by using the hierarchical structure model in system engineering, this paper firstly clarifies the logical relationship of different factors. Then, the existing trip survey data of several cities is used to establish the mathematical relationship of various factors of the structure model. Finally, the mode choice forecasting method is proposed based on the structure model of influencing factors. The case study result of six cities shows small bias, indicating that the proposed method is of great practical value. Policy makers can use the results to discover the trip structure feature and grasp the direction of transportation development policy.
A growing issue for many large cities in China is the increasingly severe traffic congestion. As the problem appears with the advent of private car age, policy makers want to solve traffic congestion problems from the aspect of trip mode. Mode split differs largely in various cities because of the different city characteristics (e.g., urban form, city scale, and population). From this point, finding out the effects of city characteristics on mode choice is important for policy makers to draw up warranted policies and measures.
Trip mode split refers to the number of traveling people shared by various trip modes within or between traffic zones. Trip mode split forecast is an important part of traffic demand forecast. The commonly used forecasting models are diversion curve model, probability model, regressive model, and so on. In addition, discrete choice model has already entered practical stage. Usually, discrete choice used for transportation system analysis is based on random utility theory [
Recently, researches in China mainly focus on the trip modes which meet the requirement of ecotransportation and sustainable development, while studies in other countries vary widely. Bergström and Magnusson emphatically discussed the influence mechanism of external factors such as natural conditions, architectural environment, and urban form. Comparatively speaking, the study of internal factors is more prevalent [
Furthermore, the discussion of public transit share rate is an important issue both in China and abroad. For instance, Thøgersen examined people’s attitude towards public transit to gain a way of switching the car mode to bus [
According to the descriptions above, most of the studies focus merely on a certain point rather than comprehensively considering the integrated transportation system. However, this paper analyzes the mode choice systemically based on the influence of various factors in Section
Hierarchical structure model is one of the system structure models, which was first introduced by Prof. John N. Warfield (the United States, 1973), is used to describe the relationship among various components as well as that between the system and the environment. The modeling approach firstly identifies the relationship of various elements (causal relations, sequence relations, affiliation, subjection relation, etc.) and then builds the system structure model, that is, abstracting a complex problem using the structure relational model which is widely used to analyze social, economic, environmental, management systems and to provide a scientific basis for system planning [
The transportation system involves many different elements, of which both the correlation and the structure are not entirely clear. Thus for further analysis, the aims of this paper are to (a) clarify the relationship of various elements through indirect relationship, (b) then establish the structural model, and (c) finally fit the model step by step using the survey data and propose a method based on influencing factors for trip mode split.
The rest of the paper is organized as follows. Section
The aims of this study are to (1) examine the interactions between trip mode choice and the influencing factors; (2) propose a sketch method for mode split based on the interactions; and (3) determine urban factors that influence mode split and address these factors in policies related to resident trip.
According to the analysis above, we should firstly build the structure model, then develop the mode split model by data fitting, and finally analyze the variable effects.
From the system view, the traffic mode structure is influenced by various factors which can be classified into internal influence factors and external influence factors. The internal factors include human factors, vehicle and facility factors (e.g., rate of private vehicle, public transit facility, and road network layout and hierarchy), and trip factors (e.g., travel distance, purpose, and time), while external factors include urban characteristics (e.g., city scale, urban form, land-use pattern, and economic level), environmental factors (e.g., natural condition, ecology, energy consumption, and land resources), and policy factors (e.g., social-economic policies and transport policies concerning management, technology, etc.).
Natural conditions can affect the trip mode choice from the following points: (1) natural barriers such as the gulf, rivers, lakes, and mountains will block transport routes or change the road network form. (2) It is difficult for bikes to adapt to the hilly area because of the large slope. (3) Regions with bad climatic conditions, such as extremely cold and high altitude plateau cities, are not suitable for bicycle trip. In addition, the city scale depends mainly on local natural conditions, as suitable natural condition is an important reason for city formation [
Traffic demand and its spatial distance distribution are determined by city scale, because trip distance elongates with the increase of urban land use, which inevitably leads to the reduction of walk and the growth of bus. The inverse relation between the city scale and the per capita trip times shows that the per capita trip times will decrease with the expansion of city scale. According to the travel survey in the cities with different population scale, the share rate of walk is higher in the medium and small cities, but relatively lower in large cities.
Urban form refers to the form of urban space pattern or the external shape, which mainly includes the following patterns: single central type (such as Beijing and Tianjin city), multicenter group type (such as Wuhan city), multicenter zonal type (such as Chongqing), zonal axial type (such as Dalian and Lanzhou), and star-shaped and other types. The increase of average trip distance in the ribbon cities is bound to induce the switch from nonmotorized transport to motorized transport mode [
Urban economy, the social development level, and the urban modernization degree all influence urban traffic demand and supply, and different transport policies would encourage or restrict a certain trip mode. The impact of urban economy includes the following: (a) the rate of cars, motorcycles, and other private vehicles increases along with the development of economy; (b) the scale of city transportation development is directly or indirectly affected by the economy, and the traffic demand increases due to the development of economy, and (c) the investment on urban transportation infrastructure rises as urban economy level increases, which will in turn improve the bus service and induce the bus trip.
The configuration of transportation facilities can affect the mode choice intensively in that sidewalk continuity, sidewalk width, presence of cycling, and walk paths will have great impact on the nonmotorized mode choice [
Generally speaking, travelers have a very strong perception of time when choosing trip mode. In short-distance travel, especially in urban internal travel, they will first consider the impact of travel time. Different traffic modes are suitable for trips of different distance. Therefore, the accumulation process of travelers in space is bound to promote different traffic modes showing different range of trip distance in space. The increased rate of private vehicle ownership will also directly lead to the augment of car mode share rate. The roads, as carriers of the passenger transportation, also have a direct impact on the mode split.
In summary, the interactive relationships of various factors are considered in Figure
Causality among influencing factors of trip mode split.
The corresponding computation of the relationship of the influencing factors (in Figure
Then set up the adjacency matrix, which is denoted by
The results are as follows:
Equation (
Set up a new set defined as the common set
Then the accessible set, antecedence set, and the intersection of them can be got on the basis of known matrix
Interlevel grade table (grade 1).
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1 | 1 | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 | 1 |
2 | 1, 2 | 2, 4, 8, 9, 10, 11 | 2 |
3 | 1, 3 | 3, 4, 8, 10, 11 | 3 |
4 | 1, 2, 3, 4 | 4, 8 | 4 |
5 | 1, 5 | 5, 7, 8, 9, 10, 11 | 5 |
6 | 1, 6 | 6, 7, 8 | 6 |
7 | 1, 5, 6, 7 | 7, 8 | 7 |
8 | 1, 2, 3, 4, 5, 6, 7, 8, 9 | 8 | 8 |
9 | 1, 2, 5, 9 | 8, 9 | 9 |
10 | 1, 2, 3, 5, 10 | 10, 11 | 10 |
11 | 1, 2, 3, 5, 10, 11 | 11 | 11 |
According to Table
Minus identity matrix from the shifted accessibility matrix
Structural model for influencing factors of trip mode split.
The practicability and significance of model study would be limited without a large amount of detailed data which can acquire a more precise mathematical model of interrelationship between all levels. The primary data used in the current analysis is drawn from transportation planning reports, which are done by the School of Transportation, Southeast University. These data include urban travel survey data and statistical information of cities in different sizes and patterns, such as Shenyang, Shenzhen, and Suzhou. Part of the statistical results is shown in Table
The structural model not only clarifies the relationship between various factors but also simplifies the method for system analysis. The calculation results of the first level variable can be directly got if we know the relationship of variable values between the fourth level and the other levels. Furthermore, the fitting process becomes easier because the structure model eliminates interaction of the same level variables as well as neglecting the linear correlation between variables of the same level.
Regression models are used to examine the mathematical interactions between different levels. For example, we can use the survey data to fit a model on the relationship between trip distance and urban forms, city scale, and travel purpose.
Variables are key elements in the analysis process, so it is essential to select the variable indicators. In order to complete the analysis, it is better to get indicators from the existing data. According to relevant research data, the variable factors are determined as follows.
Policy intensity of prioritizing public transportation.
Policy of prioritizing public transportation | Very strong (policy) | Strong (policy) | Moderate (policy) | Weak (policy) | None (policy) |
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Value | 0.75–1 | 0.5–0.75 | 0.25–0.5 | 0–0.25 | 0 |
Statistical data of urban and trip characteristics (partial data).
City | Survey year | City scale | Urban form | Facility Configuration | Total Population | Number of the vehicles | Per capita GDP | Policy | Trip purpose | Trip times | Average Travel time | Walk share rate | Time of walk | Bicycle share rate | Time of bicycle | Bus share rate | Time of bus | Car share rate | Time of car |
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Unit | — | Million people | — | km/km2 | Million people | Million vehicles | Yuan | — | — | Trips Per day and person | Min | % | Min | % | Min | % | Min | % | Min |
Bengbu | 2002 | 77.16 | 0.417 | 7.06 | 340.3 | 3.848 | 5043 | 0.75 | 0.435 | 2.86 | 23.725 | 37.71 | 18.5 | 31.53 | 21.5 | 23.68 | 31.5 | 7.08 | 22.70 |
Huzhou | 2005 | 107.72 | 0.438 | 7.4 | 271.8 | 17.6 | 18983 | 0.8 | 0.69 | 2.52 | 20.33 | 25.64 | 15.92 | 56.76 | 18.88 | 6.31 | 26.54 | 11.29 | 24.15 |
Qinhuangdao | 2005 | 73.97 | 0.43 | 4.47 | 273.3 | 26.59 | 14162 | 0.6 | 0.265 | 2.74 | 23.066 | 23.76 | 13.49 | 54.4 | 17.525 | 10.03 | 31.31 | 11.81 | 19.35 |
Shangyu | 2006 | 20.49 | 0.543 | 5.28 | 77.28 | 11.505 | 25718 | 0.15 | 0.557 | 3.25 | 17.53 | 30.53 | 16.84 | 50.27 | 18.38 | 4.99 | 27.09 | 14.21 | 16.22 |
Shenzhen | 2002 | 404.2 | 0.421 | 7.88 | 617.5 | 56 | 46003 | 0.3 | 0.531 | 1.59 | 26.5 | 53.43 | 14.75 | 25.66 | 15.97 | 12.07 | 48.37 | 8.84 | 28.92 |
Shenyang | 2006 | 492.34 | 0.597 | 5.3 | 698.5 | 53.15 | 25951 | 0.65 | 0.635 | 2.44 | 33.7 | 29.22 | 18.95 | 38.88 | 26.44 | 18.8 | 48.00 | 13.10 | 35.11 |
Shihezi | 2007 | 32.54 | 0.425 | 4.8 | 64.16 | 4.775 | 13222 | 0.3 | 0.216 | 2.95 | 23.2 | 44.34 | 17.54 | 35.11 | 25.38 | 12.12 | 28.78 | 8.43 | 21.09 |
Suzhou | 2005 | 127.94 | 0.560 | 2.25 | 576.2 | 122.5 | 21693 | 0.75 | 0.493 | 2.43 | 24.72 | 27.72 | 18.85 | 54.33 | 24.8 | 6.44 | 46.6 | 11.51 | 36.86 |
Taicang | 2003 | 13.69 | 0.445 | 6.32 | 44.89 | 12.16 | 40094 | 0.2 | 0.225 | 2.70 | 14.68 | 22.8 | 17.09 | 59.8 | 14.03 | 2.3 | 32.25 | 15.1 | 13.97 |
Weifang | 2007 | 144.16 | 0.529 | 6.1 | 852.2 | 118.24 | 17247 | 0.35 | 0.376 | 2.44 | 22.75 | 21.72 | 17.21 | 55.82 | 22.58 | 8.08 | 34.31 | 14.38 | 27.00 |
Guiyang | 2002 | 83.6 | 0.459 | 5.45 | 371.8 | 9.475 | 8110 | 0.55 | 0.529 | 2.49 | 21.83 | 62.4 | 20.28 | 4.3 | 15.28 | 26.6 | 30.27 | 6.7 | 31.25 |
Wujiang | 2003 | 45.8 | 0.416 | 4.11 | 77.3 | 8.704 | 14236 | 0.35 | 0.408 | 2.55 | 20.56 | 29.84 | 16.01 | 59.37 | 19.77 | 4.13 | 35.20 | 6.66 | 41.57 |
Yangzhou | 2009 | 110 | 0.485 | 4.89 | 452 | 17 | 12362 | 0.75 | 0.287 | 2.86 | 20.52 | 11.20 | 16.90 | 79.21 | 20.55 | 4.31 | 32.08 | 5.28 | 22.22 |
Yinchuan | 2008 | 75.82 | 0.407 | 2.58 | 137.7 | 14.9 | 11379 | 0.35 | 0.382 | 2.48 | 26.91 | 24.28 | 19.91 | 46.42 | 25.27 | 20.68 | 37.84 | 8.62 | 25.38 |
Changshu | 2010 | 25.5 | 0.346 | 4.02 | 104 | 0.917 | 24829 | 0.7 | 0.476 | 3.13 | 18.4 | 38.57 | 17.92 | 57.02 | 17.25 | 2.88 | 32.19 | 1.53 | 34.29 |
Suzhou | 2007 | 46.8 | 0.418 | 5.25 | 174 | 10.48 | 5849 | 0.65 | 0.281 | 3.05 | 27.5 | 33.12 | 20.14 | 46.8 | 24.49 | 13.2 | 30.39 | 6.88 | 36.81 |
Changde | 2007 | 66 | 0.446 | 5.59 | 607.7 | 1.92 | 13338 | 0.75 | 0.652 | 3.06 | 23.4 | 38.27 | 22.97 | 23.61 | 21.57 | 27.58 | 32.84 | 10.54 | 23.4 |
Huaibei | 2005 | 68.73 | 0.467 | 4.34 | 202 | 5.617 | 8371 | 0.65 | 0.285 | 3.57 | 22.5 | 51.42 | 20.02 | 23.08 | 27.59 | 16.83 | 29.3 | 8.67 | 35.23 |
Kunshan | 2006 | 19.30 | 0.429 | 5.1 | 120 | 7.9422 | 33936 | 0.35 | 0.551 | 2.60 | 19.05 | 31.96 | 15.891 | 55.68 | 19.16 | 3.82 | 34.36 | 8.54 | 40.29 |
Statistical data of urban and trip characteristics are shown in Table
Considering that the complicated natural conditions are difficult to quantify, it is better to work up mathematical models from the third level. The modeling process is twofold. Firstly, we provide an accurate prediction model that is calibrated by travel survey data for mode choice. Secondly, we test the model precision through a detailed validation based on external data.
Firstly, set up the relationship between trip distance
Scatter diagram of policy factors and motor vehicle population.
Set up the relationship between mode split rate
In order to capture the impact of various factors on mode choice more clearly, we use the partial least-squares regression (PLS) to fit data. PLS consists of the basic functions of multiple linear regression analysis, canonical correlation analysis, and principal component analysis, and by using PLS we can not only obtain more accurate fitting results but also analyze the impact of each factor, that is, the contribution rate of each factor (contribution, VIP, variable importance point).
Trip distance has the largest impact on the walk share rate, for walk occupying the largest proportion among the mode choice of short trips and decreasing rapidly as the distance increases. Hence, it is necessary to select the walk mode from microcosmic view. The VIP value of per capita ownership has also been over 1, because statistics in this paper are about the ownership of motor vehicle which includes motorcycle and the other two. Unlike walk mode, bicycle, bus and car mode relate to the factor of ownership. As the walk mode split rate can be regarded as total trips minus the rate of this three modes, the walk mode split rate also relates to the factor of ownership.
Bicycles, servicing primarily to short-distance travel in metropolis of our country, are greatly influenced by trip distance. In addition, other factors such as travel time cost, the per capita holdings, and facilities configuration whose VIP values are close to one also play a significant role.
Travel time has the largest effect on bus mode choice, which illuminates the obvious time-consuming characteristics of public transit mode; in other words, whether people choose public transit or not depends mainly on the travel time. So the key to increase bus travel is focusing on the reducing of bus delay and the improvement of the service level.
The number of motor vehicles itself determines the personal transport split which is also affected by road conditions, because road traffic facilities would induce private car travel. The trip distance of personal transport has a wide coverage, so distance and time-cost characteristics have little influence on it.
Concluding the above analysis, we can calculate the share rate of the various modes using formulas (
As a final extension of the research, a brief forecasting application is conducted to verify the fit goodness of the model developed above and reality system and to test whether the model can reflect the characteristics and changing rule of reality system or not. In addition, the validation process is an important way to analyze the problem-solving ability of our proposed model. Thus in order to calculate the mode split rate and analyze the validity of model, we compare the forecasting results with actual survey data taken from Wuhan, Changchun, Nanning, Hefei, Anqing, and Nantong.
We substitute the original data of city scale
Predictive values and validation results are listed in Table
Model validation results.
City |
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Wuhan | 418.22 | 0.37 | 0.507 | 0.5 | 29.876 | 29.589 | 30.60 |
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44.641 | 44.211 | 43.18 |
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Changchun | 306.824 | 0.577 | 0.354 | 0.45 | 47.263 | 46.886 | 47.38 |
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17.499 | 17.359 | 18.53 |
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Hefei | 107.49 | 0.568 | 0.223 | 0.4 | 30.011 | 29.987 | 31.34 |
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42.560 | 42.525 | 42.37 |
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Nanning | 128.3 | 0.542 | 0.456 | 0.35 | 26.265 | 26.410 | 25.26 |
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54.957 | 55.261 | 57.72 |
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Anqing | 60.3 | 0.507 | 0.316 | 0.4 | 30.800 | 30.923 | 32.70 |
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43.103 | 43.275 | 44.88 |
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Nantong | 54 | 0.436 | 0.485 | 0.3 | 30.860 | 30.699 | 32.91 |
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52.722 | 52.447 | 51.48 |
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Wuhan |
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8.506 | 8.424 | 8.62 |
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17.946 | 17.774 | 17.6 |
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Changchun |
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23.859 | 23.669 | 22.46 |
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12.182 | 12.085 | 11.63 |
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Hefei |
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16.098 | 16.085 | 18.00 |
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11.411 | 11.402 | 8.29 |
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Nanning |
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5.958 | 5.991 | 5.84 |
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12.267 | 12.335 | 11.18 |
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Anqing |
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10.301 | 10.342 | 9.12 |
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15.396 | 15.457 | 13.30 |
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From the view of data analysis, we use the factor contribution rate in the model to estimate the effects of motor vehicle population, trip distance, road traffic facilities, and travel time on trip mode split. The analysis results can provide theoretical basis for trip mode structure policy. For example, in order to improve the bus share rate, decision makers should increase the service level of public transit to make sure that the bus travel time is in the reasonable range. In addition to travel time, the travel distance also affects the trip mode split greatly. Thus, using advanced technology to collect information on passenger trip distance is very important. As for private cars, we can reduce travel in private cars from the following two aspects. On the one hand, control the private car ownership by limiting the amount of license. On the other hand, considering the impact of trip distance, increase the attractiveness of public transit to share part of the traffic volume.
It is a new attempt to develop the hierarchical structure model for modal split. The methods and models presented in this paper are not the most advanced mode split modeling tools, but they comprehensively consider nearly all aspects of influencing factors in the transportation system and reflect dynamic development process of urban trip mode structure. Moreover, calculation results which are the factual manifestation of traffic mode structure in this city can be of great help for policymakers to grasp the traffic structure in a macro level and plan the further transportation more profoundly.
However, the accuracy of decision coefficients (
In addition, compared with disaggregate models and neural networks, the proposed methods and models ignore the properties of the individual trips and do not take into account the impacts of traveler’s physical, psychological, and other characteristics on trip mode split. Therefore, the impacts of micro individual travel characteristics and macro urban characteristics on trip mode split should be analyzed comprehensively, which will be the most important improvements directions of this paper.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research is supported by the National Natural Science Foundation of China (51308298 and 51308311), Project of Ministry of Housing and Urban-Rural Development of China (2013-K5-20), Project of Jiangsu province joint innovation fund of industry, education, and research Prospective joint research project (BY2013004-04), and Project of Nanjing University of Science and Technology.