Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2015 / Article
Special Issue

Modeling and Simulation in Transportation Engineering 2014

View this Special Issue

Research Article | Open Access

Volume 2015 |Article ID 803961 | https://doi.org/10.1155/2015/803961

Long Xue-qin, Wang Jian-jun, Guan Hong-zhi, "Travel Distance on Each Grade Road of Urban City Based on Reliability", Mathematical Problems in Engineering, vol. 2015, Article ID 803961, 6 pages, 2015. https://doi.org/10.1155/2015/803961

Travel Distance on Each Grade Road of Urban City Based on Reliability

Academic Editor: Wuhong Wang
Received18 Jul 2014
Accepted12 Sep 2014
Published22 Mar 2015

Abstract

In order to reflect influence of travel time reliability on route choosing, considering traffic accidents’ influence under random state, we analyzed travel distance distribution regularity of each grade road, through traffic assignment. Travel time reliability model was produced and modified, considering the randomness of accidents, delay time, and capacity. The maximum preponderant travel range of each grade road was defined, and stochastic user equilibrium assignment was adopted to get travel turnover and distance, based on the corrected model. And then regulation of distance distribution was analyzed. Conclusion shows that reasonable travel distance of local way, distribution way, and primary way is 1 km, 2.28 km, and 3.54 km, respectively.

1. Introduction

Traditional research of travel distance was generally based on ideal environment. Investigation and statistical analysis have been applied to study the route choice of traveler and then deduce the travel distance. With the surge in the number of motor vehicles, traffic congestion is always caused by accidents; therefore the reliability of road network is particularly important. Under the principle, research on route choice is different from the traditional study, and travel distance on each grade road is also changed significantly.

The current study proposed some methods of travel distance from different perspective. Love and Morris [1] evaluated the accuracy of a range of reasonable forms for distance estimating functions using samples of urban and rural road distances. Alpaydin et al. [2] proposed to use nonparametric approaches using neural networks for estimating actual distances. Brimberg and Love [3] and Ballou et al. [4] estimated the travel distance from the weighted one-two norm and circuity factors, respectively. Schlossberg et al. [5] examined the relationship between urban form, distance, and middle school students walking and biking to and from four schools in Oregon. Golob and van Wissen [6] proposed a dynamic simultaneous equations model of car ownership and modal travel distances. Zhang et al. [7] find that travel distance of tourists to a destination is related to their demographic data, but is not related to the tourist motivation.

In China, Zhou et al. [8] discussed the distance distribution regularity, according to the function and characteristic of each grade road. The above study did not consider the actual traffic situation and travel time reliability but only took the ideal road network and travel environment into account.

In respect of time reliability, Chen et al. [9] presented a degraded time reliability model considering the influence of weather, congestion, and accidents. Clark and Watling [10] proposed a technique for estimating the probability distribution of total network travel time, in light of normal day-to-day variations in the travel demand matrix over a road traffic network. From the above, network reliability affects traveler’s route choice, and travel distance is of major difference from the past, under the premise of satisfying travel demand.

However, in the current research of travel time reliability, almost study took traffic capacity as indicator, rarely considering traffic accidents. This paper defines the maximum travel distance according to the characteristic of auto-vehicle trip. Then, incorporate traffic accidents into trip time reliability model, and take this as the basis of route choice. After the traffic assignment, analyze travel distance of each grade road and the regularity.

2. Research Procedures

(1)Build original network and define the maximum travel distance of each grade road and traffic assignment network.(2)Construct travel time reliability function , and study the results of traffic assignment, taking the maximum as target.(3)Compare the travel distance under different networks, and get the maximum distance of each grade road.(4)According to the minimum and maximum travel distance, deduce the distribution regularity of travel distance.

3. Network Constructions

3.1. Origin Network

Suppose that road network is regular grid. The grade of road is descending by one grade, and higher roads encompass some lower roads. The space between same grade roads in east-west direction is the same as south-north direction. Travelers should first go by high grade roads and then lower grade roads, conversely from lower roads to higher roads. Step over is forbidden.

According to “Urban road traffic planning and design specifications” [11] (GB50220-95), density of express way, primary roads, distribution roads, and local roads in major city is 0.4~0.5 km/km2, 0.8~1.2 km/km2, 1.2~1.4 km/km2, and 3~4 km/km2, respectively. Suppose that the average distance between roads with grade is ( means local road, means distribution road, means primary road, and means express way). In accordance with the specifications for roads space, the origin road network is shown as Figure 1.

3.2. Maximum Preponderant Travel Distance

Obviously, possible travel distance for each grade road is infinite. However, considering the convenience, preponderance for road with grade will not exist, if roads with grade higher than exist in the network. That is, roads of grade cannot be considered as the highest grade roads which travelers go through. Therefore, preponderant travel distance of road with grade means that, in the range covered by road with grade , traveler’s trip distance which the highest grade road travelers can go by is . That is diagonal trip among the range covered by road with grade .

For local roads, the maximum preponderant travel should be the diagonal trip among the range covered by distribution roads, which is shown as trip between node and node in Figure 1 (nodes and are located in the center, the same as below). Similarly, maximum preponderant travel of distribution roads, primary roads, and express ways is corresponding to node and node , node and node , and node and node .

3.3. Road Network Construction for Traffic Assignment

As shown in Figure 1, for the maximum travel range of local roads, trip distance between nodes and is , no matter which route travelers choose. Supposing that the traffic in OD is , then turnover for local roads is .

For the maximum travel range of distribution roads, travel from node to can be decomposed into three stages, concluding travel from node to , travel among the range covered by node , and travel from node to . Supposing that OD traffic between nodes and is , turnover of distribution roads from node to and node to is . Turnover of other grade roads from node to can be got by traffic assignment among the network covered by nodes , , , and .

For the maximum travel range of primary roads, travel from node to can be decomposed into three stages, concluding travel from node to , travel among the range covered by node , and travel from node to . Supposing that OD traffic between nodes and is , turnover of distribution roads from node to and node to is . Turnover of other grade roads from node to can be got by traffic assignment among the network covered by nodes , , , and .

For express way, travel space can be viewed as the range covered by nodes and (i.e., the whole network).

Based on what is mentioned above, traffic assignment should only be adopted on the area of , , and . Then, travel turnover on maximum travel range of each grade road can be got.

4. Reliability of Trip Time

4.1. Reliability of Trip Time

When choosing travel route, travelers pay close attention to trip time. However, trip time is related to road condition, but also the reliability of network. Particularly, with the augment of vehicles, congestion caused by traffic accidents frequently occurs. Therefore, traffic accidents are introduced to the reliability model.

Reliability of trip time describes the probability of road keeping unimpeded. Introducing the saturation and expectation, model of trip time reliability can be described as follows [12]:in which means roads’ condition, that is, the factors influencing road reliability except traffic state. Maximum value of is , and at this moment trip time reliability is only influenced by traffic state.

Parameter reflects traveler’s expectation. Higher expectation means larger . Consider . indicates travel time under free flow, and indicates expectation travel time. Generally, is the minimum travel time in all trip routes between OD pairs under free flow, that is, . means the free travel time for route .

means the saturation of the road.

4.2. Probability of Accidents

Supposing that accidents are event , and congestion is event , then probability of congestion caused by traffic accidents is , in which means probability of congestion if traffic accidents are occurring and means probability of traffic accidents.

Traffic accidents are random and can be seen as obeying normal distribution. Supposing that is the number of traffic accidents, and are average and variance values of the traffic accidents, respectively. Probability density function can be described as

Supposing that is forecast value of traffic accidents (number/year), then . According to probability statistics theory, standard variance can be seen as one-third of difference between average and minimum values [13]; then . Probability density function can be described asin which , .

Probability of traffic accidents is .

The number of accidents is related to traffic flow and road characteristics. For road section, , in which means length of road section (km), means traffic flow (pcu·year−1), and , , and are parameters. According to Sawalha and Sayed [14], , , and . For intersection, , in which means traffic flow on primary road (pcu·year−1), means traffic flow on minor road (pcu·year−1), and , , and are parameters , , and [15].

For road , defining total traffic accidents as the traffic accidents sum of section and intersection, then .

4.3. Probability of Congestion Caused by Traffic Accidents

Assume that, after traffic accidents, road capacity is decreased to (pcu·h−1). At this moment, traffic flow is (pcu·h−1) and congestion probability is

Probability of congestion caused by accidents is

Therefore, probability of normal traffic flow is . When traffic accidents are taken into consideration, . Function of travel time reliability can be described as

In 1996, traffic control system manual of FHWA put forward the capacity influence of urgency accidents, as shown in Table 1 [16].


Lanes in one wayLanes of congestion
123

20.350/
30.490.170
40.580.250.13

Generally, lane occupied by traffic accidents is one, and then capacity after accidents should be viewed as normal capacity multiplied by percentage corresponding to total lanes and lane of congestion in Table 1. For example, normal capacity of distribution way for 4 lanes in both ways is 1185 pcu/h and average capacity after accidents is  pcu/h.

Travel time reliability model not only reflects the expectation of travel time, but also influences the congestion caused by accidents. Then, the amended model considers comprehensive factor when choosing trip route. Travel time reliability of one route is product of all road sections; that is,

5. Travel Distance Distribution

5.1. Traffic Assignment

Traffic assignment should be adopted on the maximum travel range of distribution way , primary way , and express way , through which we can obtain the maximum travel distance. In traffic assignment, suppose the origin and destination nodes are top left corner and bottom right corner, respectively. Suppose that OD volume is , , and for the above three assignment networks.

Stochastic user equilibrium assignment is applied in traffic assignment, and principle of route choosing is reliability maximization. That is, for routes collection , if route satisfies

, then travelers will choose route .(1)For network , travel turnover of local ways is , and travel distance is .(2)For network , suppose that traffic turnovers of local and distribution ways through assignment are , . Among the range node , turnover of local ways is , and travel distance is . Travel turnover of distribution ways is , and travel distance is .(3)For network , suppose that traffic turnovers of distribution and primary ways through assignment are , . Among the range node , turnover of distribution ways is , and travel distance is . Turnover of primary ways is , and travel distance is .(4)For network , suppose that traffic turnovers of distribution, primary, and express ways through assignment are , , and . Then, among the range node , travel distance for each grade road is , , and .

Maximum distance for local, distribution, primary, and express ways is , , , and , respectively.

For simplicity, suppose that minimum travel distance of road with grade is maximum travel distance of road with grade . Therefore, the minimum travel distance of local way, distribution way, primary way, and express way is , , , and .

5.2. Travel Distance Distribution

Supposing that the average travel distance of local way is , distribution way is , and primary way is ; then [8]in which is share ratio model of vehicles. According to [17], proportion function of vehicle travel is . is travel distance, and is parameter.

6. Example

(1)According to the principle, assume road space of local way, distribution way, primary way, and express way is  km,  km,  km, and  km, respectively. The origin network and traffic assignment network can be constructed.(2)Calculate travel distance of each grade road. Suppose that OD volumes are all the same; that is,  pcu/h. Traffic turnover and distance for each grade road are shown in Table 2.(3)Travel distance distribution:distance of local way is , distance of distribution way is , and distance of local way is .


RangeLocal wayDistribution wayPrimary wayExpress way
Turnover Distance Turnover Distance Turnover Distance Turnover Distance

15001.5
4520.45225482.548////
//29462.94640544.054//
//14271.42726102.6159635.963
Max. distance1.52.94.16.0
Min. distance01.52.94.1

Travel distance distribution is shown in Figure 2.

7. Conclusions

In this paper, influence of traffic accidents on travel time reliability was considered, and a modified reliability model was proposed. According to vehicle characteristic, the maximum preponderant travel rang was defined, and four traffic assignment networks for each grade road were constructed. On the basis of new reliability model, regulation of route choosing was defined. Through traffic assignment, the maximum and minimum travel distance of each grade road were calculated. At last, an example was applied to confirm the feasibility of the model. However, only grid network was discussed, and other patterns should be considered further.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The paper was sponsored by Key Lab of Transportation Engineering of Chang’an University and Beijing University of Technology. The authors thank all colleagues and students of 905 in Traffic Science and Technology Building for their assistance in gathering data for this report.

References

  1. R. F. Love and J. G. Morris, “Mathematical models of road travel distances,” Management Science, vol. 25, no. 2, pp. 130–139, 1979. View at: Publisher Site | Google Scholar
  2. E. Alpaydin, I. K. Altinel, and N. Aras, “Parametric distance functions vs. nonparametric neural networks for estimating road travel distances,” European Journal of Operational Research, vol. 93, no. 2, pp. 230–243, 1996. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  3. J. Brimberg and R. F. Love, “A new distance function for modeling travel distances in a transportation network,” Transportation Science, vol. 26, no. 2, pp. 129–137, 1992. View at: Publisher Site | Google Scholar
  4. R. H. Ballou, H. Rahardja, and N. Sakai, “Selected country circuity factors for road travel distance estimation,” Transportation Research Part A: Policy and Practice, vol. 36, no. 9, pp. 843–848, 2002. View at: Publisher Site | Google Scholar
  5. M. Schlossberg, J. Greene, P. P. Phillips, B. Johnson, and B. Parker, “School trips: effects of urban form and distance on travel mode,” Journal of the American Planning Association, vol. 72, no. 3, pp. 337–346, 2006. View at: Publisher Site | Google Scholar
  6. T. F. Golob and L. van Wissen, “A joint household travel distance generation and car ownership model,” Transportation Research B, vol. 23, no. 6, pp. 471–491, 1989. View at: Publisher Site | Google Scholar
  7. J. Zhang, W. Geoffrey, and J. K. Du, “The travel patterns and travel distance of tourists to national parks in China,” Asia Pacific Journal of Tourism Research, vol. 4, no. 2, pp. 27–34, 1999. View at: Google Scholar
  8. W. Zhou, W. Wang, and Z. Guo, “Travel distance of different modes traveled on each grade road,” Journal of Wuhan University of Technology (Transportation Science & Engineering), vol. 33, no. 5, pp. 976–979, 2009. View at: Publisher Site | Google Scholar
  9. A. Chen, H. Yang, H. K. Lo, and W. H. Tang, “Capacity reliability of a road network: an assessment methodology and numerical results,” Transportation Research Part B: Methodological, vol. 36, no. 3, pp. 225–252, 2002. View at: Publisher Site | Google Scholar
  10. S. Clark and D. Watling, “Modelling network travel time reliability under stochastic demand,” Transportation Research Part B: Methodological, vol. 39, no. 2, pp. 119–140, 2005. View at: Publisher Site | Google Scholar
  11. Ministry of Housing and Urban-Rural Development of the People's Republic of China, “Urban road traffic planning and design specifications[S],” 1995. View at: Google Scholar
  12. X. Tang, W. Wang, and L. Cheng, “Travel time reliability assessment for traffic networks,” China Civil Engineering Journal, vol. 6, no. 40, pp. 84–88, 2007. View at: Google Scholar
  13. Y. Chen and G. Wang, “The estimation of indirect loss expectation after earthquake considering randomness of traffic demand and supply,” World Earthquake Engineering, vol. 19, no. 1, pp. 25–30, 2003. View at: Google Scholar
  14. Z. Sawalha and T. Sayed, “Evaluating safety of urban arterial roadways,” Journal of Transportation Engineering, vol. 127, no. 2, pp. 151–158, 2001. View at: Publisher Site | Google Scholar
  15. P. Greibe, “Accident prediction models for urban roads,” Accident Analysis and Prevention, vol. 35, no. 2, pp. 273–285, 2003. View at: Publisher Site | Google Scholar
  16. L. Qin and B. Smith, Characterization of Accident Capacity Reduction, Center for Transportation Studies University of Virginia, Charlottesville, Va, USA, 2001.
  17. X. Niu, W. Wang, and Z. Yin, “Research on method of urban passenger traffic mode split forecast,” Journal of Highway and Transportation Research and Development, vol. 21, no. 3, pp. 75–77, 2004. View at: Google Scholar

Copyright © 2015 Long Xue-qin 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
Views911
Downloads441
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.