1 Introduction
2 Methodology
2.1 Search strategy
2.2 Review process
3 Results
3.1 Description of the sample
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▪ accident analysis (n = 14)
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▪ non-accident analysis (n = 12)
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▪ safety measures (n = 25)
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▪ other (n = 5)
3.2 Accident analysis
Study | Type | Data source | Method | Aim | Accident type | Sample size | Time period (years) | Truck type |
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Klintschar et al., 2003 [22] | Forensic | Investigation reports | Description of the case | Describe the mechanism of the specific accident type | Dragging accident | 1 | not relevant | Long trailer |
Niewoehner & Berg, 2005 [23] | In-depth | DEKRA databasea | Descriptive statistics | Explore the characteristics of accidents and suggest safety measures | Turning accidents | 45 | 1 | Goods vehicles over 3,5 t |
Fenn et al., 2005 [24] | Accident data | STATS 19 databasea | Descriptive statistic | Investigate blind spot related accidents | Blind spot accidents | 503 | 7 | Goods vehicle over 7,5 t |
In-depth | HVCIS databasea | 17 | 3 | |||||
Cookson & Knight, 2010 [25] | Accident data | STATS 19 database a | Descriptive statistics | Assessing the effects of sideguards | Trucks overtaking and turning left vs. cyclists | 352 | 3 | Heavy Goods Vehicle |
In-depth | HVCIS databasea | 142 | 10 | |||||
Gelino et al., 2012 [26] | Accident data | Police database | Descriptive statistics | Assess the safety situation | Truck-bicycle accidents | 61 | 11 | Large trucks |
Helman et al., 2013 [18] | Accident data | STATS19a database | Descriptive statistics and exposure analysis | Understand the relative risk represented by construction vehicles | Truck-bicycle accidents | 311 | 4 | Construction vehicles |
Johannsen et al., 2015 [27] | In-depth | GIDAS databasea | Detailed reconstruction | Improve understanding of pre-accident dynamics | Turning accidents | 5 | 1 | Trucks, lorries and vans |
Seiniger et al., 2015 [28] | In-depth | GIDAS and UDV databasesa | Descriptive statistics | Analysis of speeds, behaviour, infrastructure | Turning accidents | 120 | unknown | unknown |
Conway et al., 2016 [4] | Accident data | Police database | Geospatial analyses | Explore infrastructure and demand characteristics indicative of truck-bicycle conflicts | Truck-bicycle accidents on specific routes | 122 | 3 | Large (6+ tires) and commercial vehicles (4 tires) |
Britton, 2016 [29] | Accident data | FARS databasea | Descriptive statistics | Explore characteristics of fatal accidents | Fatal truck-bicycle accidents | 78 | 1 | Large trucks |
Malczyk & Bende, 2017 [30] | In-depth | UDV databasea | Descriptive statistics | Explore potential for electronic turn-assistance | Truck-bicycle accidents | 62 | 6 | Vehicle over 12 t |
Pokorny et al., 2017 [31] | Accident data | Police database | Descriptive statistics, binary logistic model | Explore characteristics and contributory factors of accidents | Truck-bicycle accidents | 271 | 15 | Vehicle classified by police as truck, semitrailer, tanker, 1-axe trailer or 2-axe trailer |
In-depth | NPRA databasea | Descriptive statistics | Fatal truck-bicycle accidents | 13 | 10 | |||
Richter & Sachs, 2017 [32] | Accident data | Police database | Descriptive statistics | Gain knowledge to improve infrastructure design | Turning accidents | 755 | 6 | Van, delivery truck, truck without trailer, semitrailer truck |
Talbot et al., 2017 [33] | In-depth | Police investigation files | Descriptive statistics | Identify the contributory factors | Truck-bicycle accidents | 27 | 5 | Trucks over 3,5 t |
3.2.1 Risk factors
3.3 Non-accident analysis
Study | Type | Aim | Data collection | Sample size | Evaluation method | Definition of conflict | Truck type |
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Fenn et al., 2005 [24] | Behavioural / Conflict | Examine drivers’ experiences related to close proximity mirrors | Postal survey | 311 drivers | Descriptive statistics | Impression of close calls (barely avoiding an accident) | not reported |
FDS International (2010) [34] | Behavioural | Evaluate the behaviour related to roadside mirrors | Interviews | 51 drivers 20 cyclists | Descriptive statistics | not relevant | Long goods vehicle |
Frings et al., 2012 [35] | Behavioural | Examine gender differences in risk perception associated with various cycling maneuvers near trucks | On-line questionnaire | 4596 cyclists | Variance analysis (ANOVA); chi-square analysis | not relevant | Truck over 3,5 t |
Conway et al., 2013 [36] | Conflict | Assess cyclists’ exposure to multimodal conflict in urban on-street bicycle lanes | Direct observations | 50 h 35 sites 25 conflicts | Bivariate correlation analyses | To avoid a collision, cyclist must exit the bicycle lane or stop | Trucks and vans |
Twisk et al., 2013 [37] | Behavioural | Evaluate awareness programs by examining the decisions of young cyclists when in blind spot areas | Table-top models Quasi-experimental design | 62 cyclists | Mixed design ANOVA | not relevant | not reported |
Helman et al., 2013 [18] | Behavioural | Assessment of drivers’ tasks while turning left; assessment of driver errors | Accompanied driving followed by short interviews | 3 drivers | Cognitive task analysis | not relevant | Construction vehicles |
Milner et al., 2016 [38] | Behavioural | Study behaviour related to direct and indirect vision, Improve the understanding of visual processing | Literature review, road user surveys, laboratory experiments | 117 drivers 129 cyclists | Descriptive statistics and qualitative analysis | not relevant | not reported |
Mole & Wilkie, 2017 [39] | Behavioural | Examine whether mirrors delay driver responses | Simulation of driving tasks | 41 drivers | Mixed model ANOVA | not relevant | not reported |
Pitera et al., 2017 [40] | Conflict | Conduct safety evaluation of loading area located next to busy cycle street | Observation with camera | 100 h 1 site 2 conflicts | Descriptive statistics | Presence of an evasive action | Delivery truck (excluding vans) |
Behavioural | Investigate road users’ understanding of a safety measure | Intercept interviews Camera | 39 cyclists 5 drivers | not relevant | |||
Richter & Sachs, 2017 [32] | Behavioural | Examine the driving and gaze behaviour when using turn-off assistant | Simulation of routes | 48 drivers | Descriptive statistics | not relevant | Van, delivery truck, truck without trailer, semitrailer truck |
Conflict | Observe the behaviour and conflicts in right-turning maneuvers | Observation with camera | 129 h 43 sites 71 conflicts | not reported | |||
Abadi & Hurwitz, 2018 [17] | Behavioural | Investigate cyclists’ perceived level of comfort near urban loading zones | Online questionnaire | 342 cyclists | Repeated-measures ANOVA, cluster analysis | not relevant | not reported |
Pokorny et al., 2018 [41] | Conflict | Investigate cyclists’ involvement in conflicts with trucks (frequency, type and characteristics of conflicts) | On-line questionnaire | 631 cyclists | Descriptive statistics, multinomial logistic regression | A near collision, but due to the quick reactions of the cyclist and/or driver, accident averted | Large road vehicle used for carrying or pulling goods or materials |
3.3.1 Conflict studies
3.3.2 Behavioural studies
3.3.3 Risk factors
3.4 Safety measures
Study | Aim | Method | Measure | Type of measure | Type of truck |
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Fenn et al., 2005 [24] | Assess the casualty reduction potential through compulsory fitting of close proximity blind spot mirrors to HGV | Literature review, accident analysis, vehicle analysis, surveys | Compulsory fitting - Blind spot mirrors | Legislation - Vehicle equipment | All types of HGV |
Niewoehner & Berg, 2005 [23] | Investigations into the field of view | Field tests | Mirrors, Fresnel lens | Vehicle equipment - active | Goods vehicles over 3.5 t |
Knight et al., 2006 [42] | Investigate the benefits of adopting an integrated approach to several measures | Literature review, computer simulation, spray measurement, cost-benefit calculation | Sideguards, front and rear underrun protection, spray suppression | Legislation - Vehicle equipment | HGV |
Feist et al., 2008 [43] | Evaluate several concepts | Conceptual analysis, simulation, expert panel discussion | Energy-absorbing front end | Vehicle equipment - passive | HGV |
Ahrholdt et al., 2009 [44] | Development of traffic safety application | Field test | Combined perception system | Vehicle + infrastructure equipment - active | HGV (rigid truck) |
FDS International (2010) [34]. | Evaluate behaviour related to roadside mirrors | Survey | Roadside mirror | Infrastructure equipment | LGV (long heavy vehicles) |
Cookson & Knight, 2010 [25] | Inform consideration of the effectiveness of sideguards on HGV to pedal cycle accidents | Literature review, before-after comparison, accident analysis | Sideguards | Vehicle equipment - passive | HGV |
Lausnay et al., 2011 [45] | To develop and test a means of detecting cyclists | Static and dynamic test | Wireless communication system | Vehicle equipment - active | Truck |
Lakshminarayana et al., 2011 [46] | Investigation of bicyclist kinematics during side and rear-end collisions | Simulation | Energy absorbing frontal system | Vehicle equipment - passive | HCV (Heavy Commercial Vehicle, 16 t) |
Twisk et al., 2013 [37] | Evaluate awareness programs | Experiment | Awareness program for adolescent cyclists | Education | Lorry |
Thompson et al., 2013 [47] | Develop and test a system using in-vehicle three-dimensional (3D) sound as a technique for augmenting truck drivers’ situational awareness | Experiment, field test | 3D sound | Vehicle equipment - active | Truck |
Rechnitzer & Grzebieta, 2014 [48] | Estimate the effects of side underrun protection | Summarizes the types of side-underrun systems used in Europe and Asia, Expert estimation | Side underrun protection | Legislation – vehicle equipment | Truck |
Robinson & Cuerden, 2014 [49] | Estimates the probable effect of removing exemptions and achieving full retrofitting compliance in London | Prediction | Retrofitting - Side guards and mirror | Legislation - vehicle equipment | Medium (3.5–7.5 t) and Heavy (over 7.5 t) Goods Vehicles |
Beeck & Goedeme, 2015 [50] | Develop an active safety system based solely on the vision input of the blind spot camera | Experiment | Detection and tracking framework | Vehicle equipment - active | Truck |
Miah et al., 2015 [51] | Evaluate and validate sensor accuracy | Calculation | Cyclist Alert System | Vehicle equipment - active | Heavy Vehicle |
Miah et al., 2015 [52] | Present a new concept | Experiment | Cyclist Alert System | Vehicle equipment - active | Heavy Vehicle |
Islam et al., 2015 [53] | Propose the optimal controller (using particle swarm optimization technique) | Modelling | Optimal steering control | Vehicle equipment - active | A-double combination (semi-trailer) |
Davis & White, 2015 [54] | Provide overview about means utilized | not relevant | Safety programme | Management | Construction vehicles |
Summerskill & Marshall, 2015 [55] | Redesign truck concept and evaluate direct vision | Projection technique | Improved direct vision | Vehicle design | Different types of truck cabins |
Pyykonen et al., 2015 [56] | Development of a monitoring system for assisting truck driver training | Field test | Training vehicle | Education | HGV |
Jia and Cebon, 2016 [57] | Build a prototype system and test it in real time | Field test | Collision avoidance system - ultrasonic sensors on the truck | Vehicle equipment - active | Tipper truck |
Seiniger et al., 2017 [58] | Provide knowledge for testing procedures of various driver assistance systems | Field test | Driver assistance system for blind spots | Vehicle equipment - active | Single tractor |
Richter & Sachs, 2017 [32] | Evaluate driving and gaze behaviour using turn-off assistant, suggest infrastructure measures | Experiment | Turn-off assistant | Vehicle equipment - active | Van, delivery truck, truck without trailer, semitrailer truck |
Martin et al., 2017 [59] | Evaluate cost-effectiveness of a range of clustered safety measures, identify regulatory options and future research needs | Review of technologies, systematic review of literature on safety measures, CBA calculations | Direct/indirect vision, impact protection, front underrun protection, VRU airbag | Legislation - Vehicle equipment/Design | HGV (N2 and N3 category) |
3.4.1 Risk factors
3.5 Other
Study | Aim | Method | Truck definition |
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Gelino et al., 2012 [26] | Identify the safety challenges in Seattle and potential safety approaches | Literature review; review of current practices in other US cities; accident analysis; media search | Large trucks |
Helman et al., 2013 [18] | Identifying features of contractual arrangements, working practices and vehicle design that contribute to collisions between construction trucks and cyclists | Literature review; Direct and indirect visibility assessment of construction vehicles; Semi-structured interviews with stakeholders | Construction vehicles |
Pattinson and Warwick, 2014 [60] | Discuss several safety issues and measures | Overview, discussion | Trucks, Large vehicles |
Summerskill et al., 2016 [61] | Evaluating blind spots of six top selling trucks in UK | CAD-based vision projection technique | Large Goods Vehicles (N2 and N3) |
Pitera et al., 2017 [40] | Evaluate the decision-making process in implementing a risky layout of docking loading area for trucks | Interviews with decision makers | Delivery trucks |