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Highway Safety Analytics and Modeling [Pehme köide]

(University of WisconsinMilwaukee, Department of Civil and Environmental Engineering, Milwaukee, WI, USA), (Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TD, USA), (Texas A&M Transport)
  • Formaat: Paperback / softback, 500 pages, kõrgus x laius: 229x152 mm, kaal: 880 g
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128168188
  • ISBN-13: 9780128168189
Teised raamatud teemal:
Highway Safety Analytics and ModelingSuurem pilt
  • Formaat: Paperback / softback, 500 pages, kõrgus x laius: 229x152 mm, kaal: 880 g
  • Ilmumisaeg: 25-Feb-2021
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128168188
  • ISBN-13: 9780128168189
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128168189.html
Märksõnad:
Highway Safety Analytics and Modeling covers the key elements needed for making effective transportation engineering and policy decisions based on highway crash data analysis. It covers all aspects of the decision-making process, from collecting and assembling data to making decisions based on the results of the analyses. The book discusses the challenges with crash and naturalistic data, identifying problems and proposing best methods to solving them. It examines the nuances associated with crash data analysis, showing how to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes.
  • Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials, which can be challenging for students and working professionals to use
  • Provides examples and case studies for each model and method
  • Includes learning aids such as online data, examples and solution to problems
Preface xi
1 Introduction
1.1 Motivation
1(4)
1.2 Important features of this textbook
5(1)
1.3 Organization of textbook
6(5)
References
11(6)
I Theory and background
2 Fundamentals and data collection
2.1 Introduction
17(1)
2.2 Crash process: drivers, roadways, and vehicles
18(2)
2.3 Crash process: analytical framework
20(2)
2.4 Sources of data and data collection procedures
22(14)
2.5 Assembling data
36(1)
2.6 4-stage modeling framework
37(7)
2.7 Methods for evaluating model performance
44(7)
2.8 Heuristic methods for model selection
51(4)
References
55(4)
3 Crash---frequency modeling
3.1 Introduction
59(1)
3.2 Basic nomenclature
60(1)
3.3 Applications of crash-frequency models
60(3)
3.4 Sources of dispersion
63(2)
3.5 Basic count models
65(6)
3.6 Generalized count models for underdispersion
71(4)
3.7 Finite mixture and multivariate models
75(2)
3.8 Multi-distribution models
77(5)
3.9 Models for better capturing unobserved heterogeneity
82(3)
3.10 Semi- and nonparametric models
85(9)
3.11 Model selection
94(2)
References
96(7)
4 Crash-severity modeling
4.1 Introduction
103(1)
4.2 Characteristics of crash injury severity data and methodological challenges
104(1)
4.3 Random utility model
105(2)
4.4 Modeling crash severity as an unordered discrete outcome
107(12)
4.5 Modeling crash severity as an ordered discrete outcome
119(11)
4.6 Model interpretation
130(1)
References
131(4)
II Highway safety analyses
5 Exploratory analyses of safety data
5.1 Introduction
135(1)
5.2 Quantitative techniques
136(25)
5.3 Graphical techniques
161(15)
References
176(3)
6 Cross-sectional and panel studies in safety
6.1 Introduction
179(1)
6.2 Types of data
180(8)
6.3 Data and modeling issues
188(5)
6.4 Data aggregation
193(1)
6.5 Application of crash-frequency and crash-severity models
194(17)
6.6 Other study types
211(3)
References
214(5)
7 Before---after studies in highway safety
7.1 Introduction
219(1)
7.2 Critical issues with before---after studies
220(3)
7.3 Basic methods
223(9)
7.4 Bayesian methods
232(7)
7.5 Adjusting for site selection bias
239(3)
7.6 Propensity score matching method
242(2)
7.7 Before---after study using survival analysis
244(2)
7.8 Sample size calculations
246(9)
References
255(4)
8 Identification of hazardous sites
8.1 Introduction
259(2)
8.2 Observed crash methods
261(7)
8.3 Predicted crash methods
268(5)
8.4 Bayesian methods
273(4)
8.5 Combined criteria
277(1)
8.6 Geostatistical methods
278(4)
8.7 Crash concentration location methods
282(3)
8.8 Proactive methods
285(3)
8.9 Evaluating site selection methods
288(7)
References
295(4)
9 Models for spatial data
9.1 Introduction
299(1)
9.2 Spatial data and data models
300(1)
9.3 Measurement of spatial association
300(6)
9.4 Spatial weights and distance decay models
306(3)
9.5 Point data analysis
309(9)
9.6 Spatial regression analysis
318(14)
References
332(3)
10 Capacity, mobility, and safety
10.1 Introduction
335(1)
10.2 Modeling space between vehicles
336(2)
10.3 Safety as a function of traffic flow
338(2)
10.4 Characterizing crashes by real-time traffic
340(6)
10.5 Predicting imminent crash likelihood
346(2)
10.6 Real-time predictive analysis of crashes
348(6)
10.7 Using traffic simulation to predict crashes
354(10)
References
364(5)
III Alternative safety analyses
11 Surrogate safety measures
11.1 Introduction
369(1)
11.2 An historical perspective
370(1)
11.3 Traffic conflicts technique
371(2)
11.4 Field survey of traffic conflicts
373(1)
11.5 Proximal surrogate safety measures
374(9)
11.6 Theoretical development of safety surrogate measures
383(11)
11.7 Safety surrogate measures from traffic microsimulation models
394(1)
11.8 Safety surrogate measures from video and emerging data sources
395(2)
References
397(2)
12 Data mining and machine learning techniques
12.1 Introduction
399(1)
12.2 Association rules
400(3)
12.3 Clustering analysis
403(2)
12.4 Decision tree model
405(7)
12.5 Bayesian networks
412(4)
12.6 Neural network
416(7)
12.7 Support vector machines
423(3)
12.8 Sensitivity analysis
426(1)
References
427(4)
IV Appendices
Appendix A Negative binomial regression models and estimation methods
431(12)
Appendix B Summary of crash-frequency and crash-severity models in highway safety
443(26)
Appendix C Computing codes
469(8)
Appendix D List of exercise datasets
477(2)
Index 479
Dr. Dominique Lord is a professor and holder of the A.P. and Florence Wiley Faculty Fellowship in the Zachry Department of Civil and Environmental Engineering at Texas A&M University. Over the last 27 years, Dr. Lord has conducted numerous research studies in the United States, Canada, and across the world in highway design and safety. Dr. Lord's primary interests are conducting fundamental research on accident analysis methodology, new and innovative statistical methods for modeling motor vehicle collisions, and before-after evaluation techniques. He has extensive experience in data analysis techniques and developed new tools that have been used by engineers and scientists across the world. His other research interests include problems associated with the crash data collection process, safety audits, and traffic flow theory. He has had more than 150 papers published in peer-reviewed journals and more than 140 papers presented at international conferences with a peer-reviewed process.

Dr. Xiao Qin is the Lawrence E. Sivak 71 Professor in the Department of Civil and Environmental Engineering and Director of the Institute for Physical Infrastructure and Transportation (IPIT) at the University of Wisconsin-Milwaukee, USA. Dr. Qin has authored over 150 refereed journal articles, conference proceedings, and technical reports, covering the areas of highway safety, traffic operations, and GIS applications in Transportation. His research has been instrumental in identifying critical safety issues in transportation systems and addressing them using effective methodologies. He has conducted extensive research to support decision making in safety project planning and development for state and local agencies and given safety lectures in several universities. He is an Associate Editor of the Journal of Transportation Safety & Security, Journal of Urban Lifeline, and serves on the editorial board of Accident Analysis and Prevention. He received his Ph.D. in Civil and Environmental Engineering from the University of Connecticut.

Dr. Srinivas Geedipally received his doctorate from Texas A&M University and has been with Texas A&M Transportation Institute since 2005. He is currently the Program Manager for Crash Analytics Group and a Research Engineer in the Center for Transportation Safety. He has more than 75 papers published in high-standard international journals and conferences. Dr. Geedipally is an advisory board member of the Analytic Methods in Accident Research journal and a handling editor of the Transportation Research Record journal. He has participated in numerous traffic safety research projects with state and federal governments and international sponsors. He has been a key contributor in the development of the Highway Safety Manual. He is a registered professional engineer in the state of Texas. Dr. Geedipally is a two-time recipient of the young researcher award, winner of best paper award, and a Fred Burggraf award winner from the TRB.