Safety Policies in Commercial Motorcycle Operation in Nigeria A PhD - PowerPoint PPT Presentation

Institute for Transport Studies FACULTY OF ENVIRONMENT A Model for the Evaluation of Transport Safety Policies in Commercial Motorcycle Operation in Nigeria A PhD research work undertaken by Aluko O.O Under the supervision of Astrid Gühnemann; Paul Timms ITS, University of Leeds

Research Background and Objectives • Commercial motorcycles play important role in developing countries’ transport – Accessibility – Employment • Lack of regulation and enforcement lead to significant safety problems Objectives : A. Identify and understand factors contributing to the safety problem and their relationships (focus on violations) B. Develop a dynamic model to understand how driver behaviour develops and is influenced by external conditions

Case-study peculiarities • Here: Nigeria (Ado Ekiti) • Similar issues encountered in most Unavailability of data developing countries • Safety often analysed without considering possible Opposing views about the benefit feedbacks of the mode

Fieldwork survey Interviews were conducted to obtain Interviews mental pictures of stakeholders about how the system is operating. This helps to provide Quantitative data extraction reference modes, initial conditions, and constants

Data analysis Helped to identify themes and linkages Nvivo Data analysis Helped to provide an audit trail of the analysis process

Data analysis (cont’d) Development of causal Generation of a narrative network • … more violations (10) led to more enforcement capacity (1) which led to reduced drivers’ income (7 )… This was because violations (10) offered some financial benefits too (increased drivers’ income (7)).

Generation of dynamic hypothesis Excerpt from the narrative Corresponding hypothesis “Whenever violations increased, Officers enforce law by more officers were drafted to detecting and arresting increase enforcement capacity violators. (1) and match the problem. This obviously would result in increase in the probability of detection (4) and violation would go down…In this way, increasing enforcement capacity (1) could reduce the total number of violation (10 )”

Causal loop diagram corrupt practices national + corruption + in regulation and rent to index + officers' enforcement loop officers' + benefit + - from dodging + rent paid arrest prosecution political - rate influence - level of enforcement + training coverage <enforcement - + risky and - coverage> - dangerous + + + probability of arrests leading experience drivers detection detection to prosecution + loop willingness deterrence ease of + loop + to give time + joining + + trade awareness for training Drivers' + population fine and of high job violations - - loop Drivers' deterrence bribe paid returns + number of populatio effect of + + + + n loop sanction + drivers <fine and bribe paid> + high job competition reduce returns time for + drivers' training population + alcohol loop - + and drug hire + use drivers' purchse competition income and rent for + - + passengers available expensive work ownership + free time options loop capacity trade is - strenuous + - loop + + target income working + earning + ...not a + period pressure lifetime fatigue + trade + thrift saving

SD Submodels (Modules)

Stock and Flow model enforcement enforcement size. coverage. net hire total legal <equivalence of number of enforcement officers motivated for Enforcement overtime service> Workforce <Attention To Additional Officers' initial Mode> Support workforce drafting excess officers support time for time to draft removal removal more hands rest <public perception <initial Target about risk in attention> operation> Perception change in target initial target time to <public perception raise time to target reduce about risk in operation> target <public perception about risk in operation> Enforcement sub-model

Baseline Results Example Result 0.6 . 1 2,000 . 1 1 1 1 1 1 1 1 1 1 1 2 1 2 40 . 2 2 2 1 2 2 3 2 2 2 2 3 1 3 3 0.3 . Baseline result 3 3 3 1,000 . 3 3 3 3 20 . 3 interpretation : 2 0 . 3 0 . 3 • Tendency to violate 2 0 . 2 0 130 260 390 520 650 780 910 1040 1170 1300 Time (Week) • Total violations Tendency to Violate : .test_baseline . 1 1 1 1 1 1 1 1 1 total violations : .test_baseline . 2 2 2 2 2 2 2 2 2 • Enforcement coverage enforcement coverage : .test_baseline . 3 3 3 3 3 3 3 3 6,000 . 1 1 2,000 . • Total drivers 1 1 1 1 1 1 1 1 • Driver income 2 1 2 2 2 2 2 2 2 2 2 2 2 2 3,000 . 1,000 . 1 2 1 0 . 1 0 . 1 0 130 260 390 520 650 780 910 1040 1170 1300 Time (Week) total drivers : .test_baseline . 1 1 1 1 1 1 1 1 1 1 drivers' income : .test_baseline . 2 2 2 2 2 2 2 2 2

Responsiveness Testing Double recruitment Result 0.6 . 3 rate : 1 2,000 . 1 1 1 1 1 1 1 3 1 1 1 1 2 40 . 1 2 2 3 2 2 1 2 2 2 2 3 2 2 1 • Insignificant changes to 3 0.3 . 3 3 1,000 . 3 tendency to violate 3 3 3 20 . 3 2 • Insignificant changes to 3 0 . 0 . 3 total violations 2 0 . 2 0 130 260 390 520 650 780 910 1040 1170 1300 Time (Week) • Significant additional Tendency to Violate : .test_double_recruitment . 1 1 1 1 1 1 1 1 total violations : .test_double_recruitment . 2 2 2 2 2 2 2 2 2 enforcement coverage enforcement coverage : .test_double_recruitment . 3 3 3 3 3 3 3 Graph of doubled recruitment rate scenario

Responsiveness Testing Remove expensive Result ownership options : 0.6 . 1 1 1 2,000 . 1 1 1 1 1 2 1 1 1 1 1 40 . 2 • Minor changes to tendency to 1 2 2 2 2 1 0.3 . violate 3 3 1,000 . 3 3 3 3 3 3 3 3 3 20 . 3 2 2 • Substantial reduction in total 3 0 . 2 2 2 2 0 . violations 3 2 0 . 2 0 130 260 390 520 650 780 910 1040 1170 1300 • Significant reduction in Time (Week) Tendency to Violate : .test_ownership . 1 1 1 1 1 1 1 1 1 enforcement coverage total violations : .test_ownership . 2 2 2 2 2 2 2 2 2 enforcement coverage : .test_ownership . 3 3 3 3 3 3 3 3 Graph of removal of expensive ownership options scenario

Responsiveness Testing Scenario 3 Result 0.6 . Raise prosecution rate : 1 1 1 2,000 . 1 1 1 2 1 40 . 2 1 2 2 2 2 1 1 • Substantial changes to 0.3 . 1 1 1 1 1 2 3 3 2 2 1,000 . 2 3 2 3 3 3 3 3 3 3 20 . tendency to violate 3 3 2 0 . 3 • Less than expected reduction 0 . 3 2 0 . 2 in total violations 0 130 260 390 520 650 780 910 1040 1170 1300 Time (Week) • Significant reduction in Tendency to Violate : .test_raise_prosecution . 1 1 1 1 1 1 1 total violations : .test_raise_prosecution . 2 2 2 2 2 2 2 2 enforcement coverage : .test_raise_prosecution . 3 3 3 3 3 3 3 enforcement coverage Graph of increase in prosecution rate scenario

Responsiveness Testing Combination of increased prosecution and removal Result of expensive ownership 0.6 . 1 1 1 2,000 . 1 1 1 2 1 40 . 2 option s: 1 2 2 1 2 2 1 0.3 . 1 1 1 3 1 1,000 . • Substantial reduction in 1 3 3 3 3 3 3 20 . 3 3 3 3 3 2 tendency to violate 2 3 0 . 2 0 . 3 2 2 2 • Substantial reduction in total 2 0 . 2 0 130 260 390 520 650 780 910 1040 1170 1300 violations Time (Week) Tendency to Violate : ..test_ab . 1 1 1 1 1 1 1 1 1 1 total violations : ..test_ab . 2 2 2 2 2 2 2 2 2 2 • Significant reduction in enforcement coverage : ..test_ab . 3 3 3 3 3 3 3 3 3 enforcement coverage Graph of combination of increased prosecution and removal of expensive ownership options

Extracts from findings SDM can be used in modelling the system The entry method into the trade contributes to the system problem substantially Improving sanction is not the same thing as increasing enforcement capacity A leverage is achieved by a combination of measures