Truck-bicycle safety: an overview of methods of study, risk factors and research needs

Abstract

The growing numbers of cyclists either injured or killed in accidents caused by trucks have been generally regarded as a safety problem since the 1980s (McCarthy & Gilbert, Accident Analysis & Prevention 28:275–279, 1996). Indeed, in several countries, cyclists killed by a truck represent almost 30% of all cycling fatalities (Pokorny et al., Transportation Research Procedia, 25, 2017). Whilst increasing attention has been paid to this topic by road safety researchers, a scoping review of the current research has been lacking. The aim of this paper is therefore to present a scoping review of the research literature related specifically to truck-bicycle safety, including both safety analysis and measures. Out of the 1,530 documents initially identified in the first phase of this search, 43 were selected for the final analysis. The review outlines the prevailing topics studied and research methods utilized for exploring these topics. Furthermore, findings regarding accident risk factors are summarised, as the information they provide presents us with a key for implementing more efficient safety measures. Additionally, suggestions for future research needs are identified.

1 Introduction

Across the world, the number of cyclists has been increasing in many cities [1]. People are encouraged and motivated to cycle, as this type of activity improves their health, reduces the negative effects of motorised traffic, and creates more liveable and vibrant cities. At the same time, recent trends in land use and urban planning, economic development, and consumer demand have contributed to increasing the numbers of trucks driving around in these same cities [2, 3]. Because of the increasing traffic volume of trucks and bikes, their routes frequently overlap and intersect with each other in constrained urban spaces. For example, in New York City alone, 15% of the bicycle networks and 11% of the truck networks are currently overlapping [4]. Thus, encounters between trucks and bikes are relatively common.

The mere presence of trucks has been shown to contribute to higher accident risk for cyclists [5, 6] and truck-bicycle accidents usually have more severe consequences for the cyclists involved than any other types of accidents [7,8,9,10]; consequently, trucks are overrepresented in fatal bicycle accidents [11]. According to the EU accident database CARE, 283 cyclist fatalities caused by truck accidents were recorded in 2015 in the EU, which is almost 14% of all cycle fatalities in Europe. In several EU countries, this percentage rises to nearly 25% (e.g. in Denmark, Estonia, Ireland, Slovakia) [12]. Studies of fatal bicycle accidents in London have identified heavy trucks as the most frequently involved vehicle category in accidents resulting in cyclists’ deaths over the past two decades [8, 13, 14].

Less severe encounters, including conflicts, have negative consequences as well. When cyclists become involved in conflicts with trucks, their fear level significantly increases, which can affect their overall perception of risk [15, 16]. In a crowded urban environment, a truck’s presence can significantly affect a bicyclist’s perceived level of comfort [17]. Therefore, frequent interactions with trucks have the potential to deter people from cycling (both in general and in avoidance of specific areas).

Given the current promotion of urban cycling and increased safety concerns related to vulnerable road users, the topic of truck-bicycle safety has continued to grab attention from the public, media and trucking industry. A range of safety measures have been introduced in many countries to increase the safety level between bicycles and trucks, including targeted legislation, more truck safety equipment, increased awareness among both cyclists and truck drivers, and safer infrastructures. At the same time, research on the topic has grown considerably, resulting in an increased body of literature. The results of this research have been summarised in several reports [18]; however, there has been a lack of a scoping review of this literature. The aim of this paper is therefore to review the research literature related specifically to truck-bicycle safety,¹ including both safety analysis and measures, in order to outline prevailing topics and the research methods utilised to study them. Furthermore, the findings regarding accident risk factors (i.e. factors that contribute to the occurrence of truck-bicycle accidents) are summarised, as this information presents us with a key for implementing efficient safety measures. Additionally, suggestions for future research needs are identified.

2 Methodology

The type of review is a scoping review. As described by Arksey and O’Malley, a scoping review outlines the research topic, summarizes and disseminates research findings, and identifies research gaps in the existing literature, as opposed to describing research findings in detail [19]. The methodological approach to the search strategy and the review itself are described below.

2.1 Search strategy

The search was conducted in October 2018; its timespan was set to the period 1990–2018. The scholarly databases Scopus (Elsevier) and Transport Research International Documentation (TRID) were searched for the titles and abstracts of written English studies using the combination of the following key words (applying the Boolean operators “and”/“or”): truck*; hgv; heavy vehicle*; lorr*; freight; safety; blind; vulnerable; cycl*; bike*; bicyc*; conflict*; accident*. The studies were required to be made available in full-text in digital format in order to be included in this review.

The TRID database was noted to contain grey literature (i.e. standards, reports and guidelines) not published in Scopus. While grey literature is not traditionally considered to fall within literature review parameters, there has been recognition of its value and a growing acceptance of its inclusion [20, 21]. Increased digitization of databases has allowed for easier access to such literature, and the deliberate selection of material considered within the review allows for the control of source expertise. Within this review, grey literature in the form of conference papers and reports published by reputable research institutes and universities was considered to broaden the scope of the review beyond the relatively low number of published peer-reviewed journal papers in the field of truck-bicycle safety.

After excluding duplicated records, the studies were checked for their relevance by first evaluating the title and then the abstract and/or full text. Only studies that focused specifically on truck-bicycle safety were selected, and their bibliographies were scanned for additional references. As a result of this process, a total of 43 studies were identified (see Fig. 1 for the search strategy’s pathway diagram).

Fig. 1

Pathway diagram of the review’s included and excluded studies

2.2 Review process

The studies were categorised according to their main topic into four categories: accident analysis, non-accident analysis (i.e. conflict and behavioural analysis), safety measures, and others. If a study involved more than one main topic, each of these topics was considered separately (e.g. a study that applied accident analysis along with an evaluation of a safety measure contains two main topics – accident analysis and safety measures). Basic characteristics of each study (e.g. analytical method, sample size), were summarised in tables for each main topic category and further described. Risk factors for each topic category were identified and assigned to the basic elements of the road transport system (i.e. road users, infrastructure, vehicle and management). At the end, suggestions for future research needs were identified and summarised.

3 Results

3.1 Description of the sample

In total, 43 studies (21 journal papers, 11 conference papers and 11 reports) were identified for the analysis. The vast majority of studies (84%) were published from 2010 onwards (see Fig. 2). While the review was open to considering studies from 1990 onwards, no studies published prior to 2003 were found to be relevant. Most of the studies originate in the UK (n = 16), Germany (n = 7) and the USA (n = 7). As the search was limited to literature published in English, it is acknowledged that the results of the review might be biased towards literature from countries more likely to publish in the English language.

Fig. 2

Annual numbers of published studies

The main topics discussed within the reviewed studies were categorised into the following groups:

• ▪ accident analysis (n = 14)

• ▪ non-accident analysis (n = 12)

• ▪ safety measures (n = 25)

• ▪ other (n = 5)

As described in the methodology, a study could be categorised into more than one group; thus, the numbers above include double-counting. The findings for each of these four groups are summarised in paragraphs 3.2.-3.5.

3.2 Accident analysis

Fourteen studies that contain an analysis of truck-bicycle accidents (referred to hereafter as TCA) were identified. Their characteristics are summarised in Table 1. There are three common types of analysis in the sample – in-depth, accident data and forensic. Three studies combine both in-depth and accident data analysis. Several studies apply accident analysis as a basis for consequent research (e.g. to explore a safety measure’s potential), while in the other studies, accident analysis is the main objective.

Table 1 Characteristics of accident studies

Nearly all of the studies apply descriptive statistics to analyse their data, while only one study applies statistical modelling (i.e. binary logistic regression - [31]). Additionally, only one study attempted to evaluate an exposure and calculate a relative accident risk [18].

The average sample size for accident data analysis is 306 accidents (min 61, max 755, st.d. 221), while it is 54 accidents for in-depth analysis (min 5, max 142, st.d. 51). The average study period is 6.3 years (st.d. 4,4) for accident data analysis and 5.1 years (st.d. 3,8) for in-depth analysis. Accidents that are connected with blind spots and right-turning trucks (left-turning in the UK) are specifically considered in six studies (referred to hereafter as turning-accidents). The definition of the term truck is not consistent within the studies. A number of studies provide a specific definition (e.g. large-6+ tires-commercial vehicles), while others provide only a vague definition (e.g. heavy goods vehicle).

3.2.1 Risk factors

Several risk factors were identified within the reviewed accident studies. These related to road users are cyclists’ incorrect assumptions about the truck driver’s ability to see them and about truck manoeuvres [23], improper adjustment and usage of blind spot mirrors by drivers [24], lack of awareness regarding blind spot issues by both cyclists and drivers [23], lack of visual contact and communication between driver and cyclist (communication breakdown) [31], risky behaviour of both cyclists and truck drivers (e.g. using phones, overtaking a truck from the inside; truck reversing on cycle paths without any outside assistance; risky overtaking of cyclist or unexpected truck turning manoeuvres) [24, 31, 33]. The risk factors related to infrastructure are complexity of urban intersections [33], objects limiting visibility (e.g. greenery; traffic signs; advertisements) [23, 31], road narrowing (e.g. due to traffic calming; parked vehicles; pedestrian facilities or road works) [31, 33], road surface conditions [31] and unsafe infrastructure layout (e.g. road moved due to construction; an alignment of cycle paths encouraging higher speeds; simultaneous green phase; broad strip of grass between traffic lane and cycle path; a traffic lane shared by cyclists moving straight ahead with adjacently turning trucks; a cycle lane or path ending at an intersection without continuing further; unsafe design of cycle advanced box; confusing road markings; pedestrian guard rails and kerbstones preventing cyclists’ escape) [23, 31,32,33]. The risk factor related to vehicle are design of construction and rigid trucks [18, 23, 31], insufficient truck equipment (e.g. lack of Class VI mirrors) [33] and limited visibility (both direct and indirect) from the truck, particularly during turning manoeuvres [23, 24, 27, 33]. The risk factors related to management are planning and management practices contributing to the overlap of bike and truck routes [4, 26], the overlap of truck and cyclist peak traffic at specific times of the day/week [33], the lack of safety near the construction sites [31] and unsuitable locations of areas with higher demand for trucks [4].

3.3 Non-accident analysis

Non-accident analyses include studies of truck-bicycle conflicts and behavioural aspects related to truck-bicycle encounters. Twelve such studies were identified. Three studies included both conflict and behavioural analysis, resulting in a total of five conflict and ten behavioural studies (with double-counting). These characteristics have been summarised in Table 2.

Table 2 Characteristics of non-accident studies

3.3.1 Conflict studies

All five studies define conflict by using the concept of an evasive action subjectively acknowledged by the researcher. Data on conflicts were collected either by an observation in real traffic using a camera or a human observer (3 studies), or by a retrospective postal or online survey (2 studies). To evaluate the data, three studies used descriptive statistics, while two studies applied a correlation and regression analysis. The three studies that used observations recorded in total 98 conflicts within 277 observation hours. Each study evaluated different types of conflicts (i.e. turning; parking; delivery manoeuvres). The two studies that collected data from surveys had a relatively high number of respondents (311 drivers and 631 cyclists), who reported in total 304 and 378 conflicts respectively experienced during the last 12 months. One of these studies investigated truck-bicycle conflicts in general, while the other one examined a specific type of conflict.

3.3.2 Behavioural studies

The 10 identified behavioural studies focused either on truck drivers (n = 4), cyclists (n = 3) or on both drivers and cyclists (n = 3). People’s ability to deal with additional equipment in trucks was their most common topic of interest (n = 4). The methods of data collection varied greatly, including postal and online surveys, interviews, observations, experiments, assisted driving and simulations. The average number of respondents was 637 (min 3, max 4596), with the lowest numbers in experiments and assisted driving, while the highest was in online surveys. To evaluate the data, ANOVA tests and descriptive statistics were typically applied.

3.3.3 Risk factors

Several risk factors were identified within the reviewed non-accident studies. These related to road users are young age, as adolescent cyclists have difficulties practicing safe performance in blind spot areas near trucks [37], bbehavioural adaptation to safety measures [34], combination of factors affecting the likelihood of driver errors [18], cyclists’ behaviour not conforming to normal expectations [18], driving in unfamiliar locations [18], gender (female cyclists might not correctly differentiate between the risks associated with inside and outside overtaking of trucks compared to male cyclists) [35], reaction time (slower reaction of drivers to objects visible only in mirrors compared to direct viewing through the front windscreen) [39] and time pressure related to delivery time slots for truck drivers [18]. These related to infrastructure are insufficient layout of loading area [40], lack of recognizable and comprehensible intersection design [32], narrow roads and tight corners [18], unseparated signalling phases for turning trucks and straight riding cyclists, particularly when traffic volumes and speeds are high [32] and specific configuration of bicycle lane and parking lane [36]. One factor related to management is related to land use characteristics, that affect the flow of trucks and cyclists [36].

3.4 Safety measures

In total, 25 studies that develop, test or evaluate truck-bicycle safety measures were identified. Their characteristics are summarised in Table 3.

Table 3 Characteristics of safety measure studies

Sixty percent of the studies discuss solely the measures related to trucks ‘equipment, particularly developing and/or testing a novel type of measurement using field tests, experiments, modelling or simulations. Most of these measures (n = 10) were active safety measures, aiming at blind spot elimination and cyclist detection in the proximity of trucks. Five studies evaluate the potential effects of implementing vehicle-related measures based on a change in legislation (e.g. retrofitting certain types of trucks with blind spot mirrors). Six studies relate to infrastructure, education and management-related safety measures.

3.4.1 Risk factors

Several risk factors were identified within the reviewed non-accident studies. These related to road users are behavioural adaptation to safety measures [34], efficiency of mirrors highly depends on the truck driver’s alertness [50], challenging scanning of multiple mirrors in high workload situations [55] and truck drivers’ overload with physical and cognitive tasks, which affect the driving performance, particularly in turning manoeuvres [51, 57]. These related to vehicle are frequent false positive alarms of an active safety system (they are annoying for truck drivers and can cause them to avoid using this system) [50], off- tracking of large trucks in turning manoeuvres (i.e. the last axle is not able to follow the first axle) results in the truck encroaching on the area where cyclists travel [53, 57], sound insulation of the truck cab can contribute to the reduction in drivers situational awareness around their truck [47] and a typical detection system warns only one of the two participants about each other’s presence [45].

3.5 Other

Five studies did not completely fit into any of the three abovementioned categories. As a result, their characteristics are summarised separately in Table 4. These studies have contributed to the overall body of knowledge by using specific methods of investigation, for example by interviewing decision-makers, searching the media or evaluating the blind spots of existing trucks through using a vision projection technique.

Table 4 Characteristics of other studies

The additional risk factors identified in these studies are difficult route planning to avoid interactions with cyclists [18], lack of consideration of freight planning within urban planning [40], lack of ownership (and awareness) of road risk by clients and contractors in the construction industry [18] and that road risk is seen as less important than general health and safety risks in the construction industry [18].

3.6 Recommendations for future research

The reviewed studies identified numerous recommendations for future research, which may be categorised into four groups - the impact of measures to improve drivers’ indirect vision (1), trucks’ design to improve direct vision (2), behavioural aspects (3) and evaluation of safety measures (4).

3.6.1 The impact of measures to improve drivers’ indirect vision

The development and application of various measures aiming to improve drivers’ indirect vision put increased demands on their users (particularly truck drivers). It is still not obvious what strategies are used by truck drivers to establish situational awareness of road users’ location in close proximity to their truck, what is the actual task time and what are the mirror use strategies [61]. In addition, the interaction between certain cognitive truck drivers’ tasks and reliance on their indirect vision to detect cyclists has not been examined [38]. The ergonomic, occupational, physiological and psychological effects of information and assistance systems on the drivers should be further explored as well [23, 30]. An investigation of annoyance levels among drivers caused by too many alarms/alerts associated with the vehicle systems is needed, as it affects the vehicle systems’ efficiency levels [47]. Alternative interfaces (e.g. vocal or visual display alert) present another field for further research [47].

3.6.2 Trucks’ design to improve direct vision

The truck’s design determines the driver’s direct vision; therefore, the variability of design features which contribute to the size of blind spots should be examined [61]. The components of optimum cab design should be defined in particular, [38] and the direct vision standards for use by vehicle manufacturers developed [55]. The new design concepts require the development of training procedures for drivers to obtain expert driving skills when driving the latest cab designs [38].

3.6.3 Behavioural aspects

Aspects of frequent misunderstanding in truck driver-cyclist interactions require further examination [30]. For example, cyclists’ decision-making skills when encountering trucks; the effects of different levels of cyclists’ salience on direct and indirect visibility [38], and the effects of stationary or moving trucks on cyclists’ risk perception in passing manoeuvres [35] present important research topics. Furthermore, the relationship between findings from behavioural studies and accidents’ causation is lacking [17, 39].

3.6.4 Evaluation of safety measures

While there have been numerous studies completed on safety measures, cost-benefit and feasibility studies on these measures are lacking [33]. Safety evaluations require an examination of measures’ safety implications before they are either deployed in vehicles or otherwise implemented [38]. It would be helpful to gain a deeper knowledge of pre-crash scenarios and better understand the potential impact of safety measures [27]. In the case of infrastructure measures reducing space capacity for truck traffic (e.g. caused by traffic calming), the short and long-term implications related to truck operations, costs, and externalities are often unknown [4].

4 Discussion

4.1 Methods and results

The methodologies identified in this scoping review may be categorized as accident analysis (i.e. police statistics, in-depth studies, forensic studies), non-accident analysis (i.e. conflict and behavioural studies) and safety measure evaluations (e.g. field tests, computer simulations, cost-benefit analysis, experiments). It is necessary to consider national/local conditions carefully when interpreting, comparing or transferring the results of these studies, as local/national differences in infrastructure (e.g. degree of segregation), legislation (e.g. compulsory truck safety equipment, cycling legally on sidewalks), enforcement (e.g. time and area restrictions on truck traffic), and/or land use (construction activities in residential areas) impact results. The different definitions of trucks used in the studies must be taken into consideration as well.

4.1.1 Accident analysis

The fact that trucks are overrepresented in fatal and severe bicycle accidents has been recognised as early as the 1980s and 90s (e.g. [13]); however, according to this review, studies related specifically to truck-bicycle safety concerns were not conducted until later,(beginning in 2003). There is strong consensus regarding the high level of severity and typical characteristics of truck-bicycle accidents (TCA) within the accident studies. Typically, accident data used in these studies are police records. As most TCA occur in urban environments, all the studies focus on urban areas. Moreover, TCA results occur infrequently in small sample sizes, even if long study periods are applied. For instance, if one considers cities in particular, even 10–15 years can be too short a period to collect enough data for meaningful analysis (e.g. in Seattle, USA,61 TCA were recorded over an 11-year period, and in Trondheim, Norway, 19 TCA were recorded over a 15-year period [26, 31]). These small sample sizes and long study periods limit the usage of statistical modelling; therefore, it is typically only descriptive statistics that are applied in accident studies. Furthermore, during long study periods, the external conditions may have changed (e.g. new legislation implemented, new trucks introduced), which is not reflected in the studies. Additionally, potential underreporting and insufficient or inconsistent accident data quality might decrease the reliability of accident studies. Another limitation is the lack of data on exposure (e.g. data on specific vehicle types involved in TCA).

The in-depth analysis of fatal accidents enables us to explore the detailed characteristics of TCA, particularly those related to accident causation and, consequently, complementary studies that use more general police accident data. Because of the high share of fatal TCA, in-depth studies are common in truck-bicycle safety analyses (8 of 14 accident studies in this review contain an in-depth analysis). When interpreting the results of in-depth studies, smaller sample sizes and longer study periods (e.g. a Norwegian study contained 13 fatal TCA gathered over a 10-year period [31]) must be taken into consideration. As the fatalities in TCA are almost always cyclists, the in-depth studies did not provide any data from cyclists’ point of view.

There was one forensic study identified in this review; however, as it analysed just one specific TCA, this did not allow for any generalisation. These types of studies are able to reveal the medical details regarding the injuries the cyclists suffer when involved in TCA, as was shown in a UK study that looked at the consequences surrounding cyclists’ severe and fatal accidents. According to this study, cyclists injured in TCA suffered severe injuries and death as a result of uncontrollable haemorrhages.² Having an awareness of this injury profile may aid prehospital management staff and expedite patients’ transfer to trauma centre care [8].

4.1.2 Non-accident analysis

When evaluating safety, non-accident studies are an alternative method of accident analysis due to the fact that these studies evaluate either conflicts or behaviour.

Conflict studies use conflicts as surrogate safety indicators. Nonetheless, while the numbers of truck-bicycle conflicts are higher than accidents, they are not as frequent as, for example, car-bicycle conflicts. Therefore, conflict studies require relatively long observation periods to gather enough data, and the usage of modern technology is vital to processing the data (e.g. software for detecting road users in video recordings). Retrospective surveys present another method of gathering data on conflicts; however, this method suffers from several well-known limitations as well (e.g. recalling bias). It is challenging to generalise the results of conflict studies, as each of them analyse different locations, manoeuvres or situations (e.g. loading area, turning manoeuvre, driving with turn-off assistant). Furthermore, albeit all reviewed conflict studies have recognised the conflicts based on an evasive action, the threshold between evasive and normal action was identified subjectively without any quantification. This potentially contributes to the different conflict rates observed between studies - one study identified two conflicts during 100 h of observation, while the other one identified 71 conflicts in 129 h [30, 41].

The low number of conflicts combined with technical and methodological challenges connected with the data collection and evaluation highlight the need for behavioural studies. These can take the form of an observation (both in real traffic and a simulator) or a survey. Such types of studies are suitable for evaluation and testing of novel equipment (e.g. human-machine interface), studying perceived risk or observing the interactions between road users. Behavioural studies provide not only valuable insight into the behavioural aspects of truck-bicycle encounters but also have the potential to interpret the findings from accident analysis. For example, the overrepresentation of female cyclists was reported in several accident analyses [23, 30, 32]. The behavioural study conducted by Frings et al. reported that females perceive risk differently when making certain manoeuvres around trucks [35], while Abadi and Hurwitz determined through another behavioural study that females’ perceived level of comfort differed substantially when bicycling in high-volume traffic or truck traffic [17]. At the same time, behavioural studies may highlight a phenomenon not seen in accident studies. For example, adult cyclists are the group most frequently involved in TCA [33], while a behavioural study found that young adolescent cyclists have difficulties in dealing with blind spot areas around trucks and could therefore be a suitable target group for receiving some kind of educational measures [37].

The validity of non-accident studies (i.e. whether unsafe behaviour or the presence of conflicts are an indicator of actual risk) presents another crucial issue when interpreting their results. There has not been any validation study conducted thus far to link truck-bicycle conflicts with accidents specifically. Moreover, this type of study is likely impossible to conduct due to the infrequency of relevant accidents.

However, as accidents are very rare, the shift towards behavioural studies can be expected, ones that not only focus on driver-machine interactions but also on driver-cyclist interactions. Furthermore, the recent development of autonomous vehicles highlights the need for behavioural studies, as they can gather knowledge of cyclists’ behaviour when in proximity with trucks in different traffic situations and settings.

4.2 Risk factors

It is widely agreed that limited visibility (both direct and indirect) is the most serious risk factor for TCA. Nevertheless, a wider range of risk factors related to all components of the road transport system has been identified in this review. This list of risk factors is useful and informative when trying to understand truck-bicycle encounters; however, it does not allow for any quantification of risk factors’ effects. As the data about exposure is usually unavailable, it is often impossible to estimate the risk factors’ magnitude. A rare example of including exposure into the risk analysis may be found in a study from the UK, which concluded that rigid trucks (particularly ones related to the construction industry) are overrepresented in truck-bicycle accidents [18].

The risk factors identified by non-accident studies are typically more detailed and concrete than those from accident studies. For example, reduced visibility has been identified by accident studies as being the most frequent risk factor, while non-accident studies are able to go deeper and identify factors that may contribute to reduced visibility (e.g. slower drivers’ reaction times to mirrors or cognitive overload). Furthermore, non-accident studies have the potential to reveal risks that are hidden within an accident analysis, e.g. the survey between involved stakeholders may reveal risk factors within the decision-making process [40]. Risk factors identified in the safety measures studies were risks directly related to the new measures implemented.

The identified risk factors predominantly focus on vehicles, road users or infrastructure. Given the complexity of transport system, risk factors existing in all its levels, including those related to the transport system management, urban and transport planning and legislation, should be analysed as well. If this analysis were to take place, it would, ideally speaking, consider their mutual interactions and influence.

4.3 Safety measures

Regarding safety measures, much of the current research focuses on improving direct and indirect visibility, which, as stated previously, has been identified as being the most severe risk factors in truck-bicycle safety. Specifically, there is an emphasis on active safety measures implemented in the trucks and issues related to the interactions of truck drivers with these novel measures. Passive safety measures, such as forgiving truck design, can also lessen the consequences of truck-bicycle accidents, but are paid less attention in the current literature specific to truck-bicycle accidents.

As risk factors exist in all levels of the road transport system, more systematic measures need to be studied as well because these measures could reduce the opportunities for trucks and bicycles to encounter one another in the first place. This could be done, for example, by infrastructure segregation (cycle paths), traffic management segregation (designated signal phases), network segregation (designated truck routes, access limitation), or time segregation (certain times for truck deliveries). The layout of sensitive locations (e.g. docking areas or construction sites) should be planned in cooperation with stakeholders from urban freight, transport and safety fields. Yet these solutions will only have an impact on the specific locations where they are implemented unless they are considered more comprehensively at the highest level of the road transport system and included within legislative and policy measures. Other policy measures to consider include ones related to retrofitting trucks or targeted education and training. Additionally, the potential impact of land use planning, traffic planning and urban logistics to generate/influence truck traffic must be considered.

Before implementing any efficient safety measures, they must first be evaluated. Future research should therefore provide further data for conducting evaluations, including a cost-benefit analysis of the proposed measures. So far, only a few cost-benefit studies have been conducted (particularly at the EU level) on the retrofitting of specific truck categories with blind spot mirrors or side guards [24, 25]. The effects of clustering the measures should be considered as well.

5 Conclusion

The increasing number of cyclists and trucks, and the severe consequences of their encounters, have increased interest in conducting truck-bicycle safety research and implementing knowledge-based safety measures. This study examines the existing literature on truck-bicycle safety within a scoping review. The review compiles the existing research on the topic and considers the methods used, risk factors identified, and future research needs. The reviewed literature falls under the categories of accident and non-accident (conflict and behaviour) studies as well as studies of safety measures. Accident and conflict studies examine past events in order to draw conclusions from dangerous encounters between trucks and bicycles; but as these events are rare, they may be complemented with behavioural studies aiming to understand how these road users behave during encounters. Several accident risk factors were identified from the studies. Within the current literature, these have generally focused on risks related to vehicles, road users and infrastructure. At the same time it has been suggested that there is a need to consider risk factors related to management, planning, and legislation as well. Having knowledge of risk factors contributes to implementing efficient safety measures, and studies of safety measures have also been identified in the review. These studies are useful for evaluating the impact of efforts to reduce risk and improve safety associated with truck-bicycle interactions. While existing studies have focused on direct and indirect visibility, there is also a need to consider system-level measures related to policy, planning, design and operations.