Skip to main content
SpringerLink
Log in

Driver Compliance Model Under Dynamic Travel Information with ATIS

  • Conference paper
  • First Online:
Information Technology and Intelligent Transportation Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 454))

Abstract

This study explores many details of the drivers response to dynamic travel information with variable message signs (VMS) which is the one of the most common advanced traveler information systems (ATIS) deployed in many areas all over the world. A stated preference (SP) survey was conducted in some parts of China to collect various drivers behavior information with VMS. Based on the findings from the surveys, seventeen potential affecting factors for driver compliance with VMS are identified and applied to further study. A binary logistic regression model is adopted to evaluate the significance of these seventeen factors. Gender, age, whether full-time worker, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, and crowded level on an alternate route are proved to be significant variables affecting driver compliance under dynamic travel information with VMS. Classification and regression trees (CART) is adopted to develop the driver compliance model. The CART model reveals the hierarchical structure of driver compliance and produces many interesting findings. The developed model is evaluated with collected data from SP survey and shows a reasonable performance. The CART model explains the behavior data most clearly and maintains the highest prediction rate.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lee C (2004) Developing driver compliance based operations model for ATIS applications. University of Wisconsin, Madison

    Google Scholar 

  2. Abdel-Aty MA, Kitamura R, Jovanis PP (1997) Using stated preference data for studying the effect of advanced traffic information on drivers route choice. Transp Res Part C 5(1):39–50

    Article  Google Scholar 

  3. Peeta S, Ramos JL, Pasupathy R (2000) Content of variable message signs and on-line driver behavior. Transportation Research Board, Washington D.C

    Google Scholar 

  4. Chatterjee K, Hounsell NB, Firmin PE (2002) Driver response to variable message sign information in London. Transp Res Part C 10(2):149–169

    Article  Google Scholar 

  5. Tsirimpa A, Polydoropoulou A, Antoniou C (2007) Development of a mixed multi-nomial logit model to capture the impact of information systems on travelers switching behavior. Intell Transp Syst 11(2):79–89

    Article  Google Scholar 

  6. Bekhor S, Albert G (2014) Accounting for sensation seeking in route choice behavior with travel time information. Transp Res Part F 22(1):39–49

    Article  Google Scholar 

  7. Dia H, Panwai S (2007) Modelling drivers compliance and route choice behaviour in response to travel information. Nonlinear Dyn 49(4):493–509

    Article  MATH  Google Scholar 

  8. Dia H, Panwai S (2010) Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information. Transportmetrica 6(4):249–270

    Google Scholar 

  9. Peng JS, Guo YS, Fu R et al (2015) Multi-parameter prediction of drivers lane-changing behaviour with neural network model. Appl Ergon 50(1):207–217

    Article  Google Scholar 

  10. Breiman L, Friedman J, Olshen R et al (1984) Classification and regression trees. Chapman & Hall/CRC, London

    MATH  Google Scholar 

Download references

Acknowledgments

This paper is supported by Program of Doctoral Scientific Research Foundation of National Police University of China (Traffic Congestion Alarming based on GPS Equipped Vehicles), Training Program of the Major Research Plan of National Po-lice University of China (Study on Road Traffic State Extraction based on Locating Point Group of GPS Equipped Vehicles), General Project of Liaoning Provincial Education Department of China (L2015554), Technology Research Program of Ministry of Public Security of China (Study on Road Traffic State Extraction based on Locating Point Group of GPS Equipped Vehicles) and Project of Natural Science Foundation of Liaoning Province (Dual-mode Traffic Guidance Models and Infor-mation Releasing policies).

Author information

Authors and Affiliations

  1. National Police University of China, Shenyang, 110035, China

    Ande Chang & Yi Jin

  2. College of Engineering, Shenyang Agricultural University, Shenyang, 110866, China

    Jing Wang

Authors
  1. Ande Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ande Chang .

Editor information

Editors and Affiliations

  1. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad Faculty of Engineering, Arad, Romania

    Valentina Emilia Balas

  2. Bournemouth University, Poole, United Kingdom

    Lakhmi C. Jain

  3. School of Information Engineering, Chang'an University, Xi'an, China

    Xiangmo Zhao

Appendix

Appendix

1. Driver Survey Questionnaire Demographic characteristics of drivers

Where are you living?

(a) Northeast (b) North China (c) East China (d) Central South (e) Northwest (f) Southwest

What kind of city you are living?

(a) Village (b) County (c) Common City (d) Provincial Capital (e) Municipality What is your gender?

(a) Woman (b) Man

What is your age?

(a) Less than 20 (b) 20 29 (c) 30 39 (d) 40 49 (e) 50 59 (f) Greater than 60

Have you ever get married?

(a) No (b) Yes

What is your degree?

(a) Elementary School (b) Middle School (c) University (d) Master or Doctor

What is your job?

(a) Student (b) Company Staffer (c) Civil Servant (d) Teacher or Doctor (e) Freelancer (f) other

Are you engaged in full-time work?

(a) No (b) Yes

How much is your monthly income (Yuan)?

(a) Less than 2000 (b) 2000 4000 (c) 4000 8000 (d) Greater than 8000

2. Perceptions of dynamic travel information

How serious do you think about traffic congestion around your hometown?

(a) Nothing serious (b) Generally serious (c) Very serious (d) Not sure

Do you feel that the availability of travel information in regards to traffic congestion is important?

(a) Not important (b) Generally important (c) Very important (d) Not sure

3. Driver behaviors under dynamic travel information

Please recall an experience of your travel, and then we will ask you some questions about how you make your decision under traffic congestions and travel information with VMS. If you cant get any experience, please give us your estimation based on the following questions. How do you think the length of current route?

(a) Less than 0.5 km (b) 0.5 1 km (c) 1 2 km (d) 2 4 km (e) 4 8 km (f) Greater than 8 km (g) Not sure

How do you think the travel time of current route?

(a) Less than 5 min (b) 5 10 min (c) 10 20 min (d) 20 40 min (e) Greater than 40 min (f) Not sure

How do you think the crowded level on the current route?

(a) Lowest (b) Lower (c) Upper (d) Highest (e) Not sure

How do you think the vehicle queue length of the current route?

(a) Less than 50 m (b) 50 100 m (c) 100 200 m (d) 200 400 m (e) 400 800 m (f) Greater than 800 m (g) Not sure

How do you think the delay of the current route?

(a) Less than 2 min (b) 2 5 min (c) 5 10 min (d) 10 20 min (e) 20 40 min (f) Greater than 40 min (g) Not sure

Are you familiar with the alternate route?

(a) Lowest (b) Lower (c) Upper (d) Highest (e) Not sure

How do you think the length of alternate route?

(a) Less than 0.5 km (b) 0.5 1 km (c) 1 2 km (d) 2 4 km (e) 4 8 km (f) Greater than 8 km (g) Not sure

How do you think the crowded level on the alternate route?

(a) Lowest (b) Lower (c) Upper (d) Highest (e) Not sure

How do you think the anticipated travel time saving on the alternate route?

(a) Less than 2 min (b) 2 5 min (c) 5 10 min (d) 10 20 min (e) 20 40 min (f) Greater than 40 min (g) Not sure

Based on all given questions above, would you take an alternate route?

(a) Yes (b) No

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Chang, A., Wang, J., Jin, Y. (2017). Driver Compliance Model Under Dynamic Travel Information with ATIS. In: Balas, V., Jain, L., Zhao, X. (eds) Information Technology and Intelligent Transportation Systems. Advances in Intelligent Systems and Computing, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-319-38789-5_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-38789-5_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-38787-1

  • Online ISBN: 978-3-319-38789-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation