New technologies in the transport sector promise to bring about certain benefits, for example related to mobility or safety, but are also expected to cause negative side effects which are in conflict with strategic city objectives. These might be mitigated by appropriate policy interventions that are designed carefully and timely. The Horizon 2020 research project LEVITATE has investigated multiple impacts of connected and automated transport systems, using an integrated multi-method approach, ranging from microscopic and mesoscopic simulations to system dynamics and Delphi panels. In particular, the system dynamics model described in this paper served to assess several systemic and wider impacts that were found difficult or even impossible to assess with the other methods. The main parts of the model are a population sub-model, a simplified traffic demand model distinguishing between three modes of transport, and a model for the use of public space. In order to calibrate the model and get results that are consistent with those of other methods, the input and output data of those simulations have also been incorporated into the system dynamics model. By means of selected example impact variables – modal split, demand for parking space and average commuting distance – the obtained effect of increasing automation as well as of several policy interventions is demonstrated.@en

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately. This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated. The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.

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Automatic incident detection (AID) systems and variable speed limits (VSLs) can reduce crash probability and traffic congestion. Studies based on loop detector data have shown that AID systems decrease the variation in speeds between drivers. Despite the impact on driver behaviour characteristics, most mathematical models evaluating the effect of AID systems on traffic operations do not capture driver response realistically. This study examines the main factors related to driver speed compliance with a sequence of three VSLs triggered by an AID system. For this purpose, the variable speed limit database of the executive agency of the Dutch Ministry of Infrastructure and Water Management (Rijkswaterstaat) was integrated into the UDRIVE naturalistic driving database for passenger car data collected in the Netherlands. The video data were annotated to analyse driver glance behaviour and secondary task engagement. A logistic regression model was estimated to predict driver speed compliance after each VSL in the sequence. The results reveal that the factors predicting compliance to the VSLs differ based on which of the three VSLs the driver is subjected to. Low speeds and accelerations before the gantry, approaching a slower leader, high proportion of time with eyes-on-road and close consecutive gantries were associated with high compliance with the first VSL in the sequence (i.e., indicating a speed limit of 70 km/h with flashing attention lights). Low speeds and accelerations before the gantry, close consecutive gantries and a small number of lanes resulted in high compliance with the second VSL (i.e., a speed limit of 50 km/h with flashing attention lights). Low speeds before the gantry and close consecutive gantries were linked to high compliance with the third VSL (i.e., indicating a speed limit of 50 km/h). Although further investigations based on a larger sample are needed, these findings are relevant to the development of human-like driving assistance systems and of traffic simulations that assess the impact of AID systems on traffic operations realistically.

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The effects of campaigns using tangible incentives (rewards) to promote safety belt usage have been evaluated by means of a meta-analytic approach. Two coders extracted a total number of 136 short-term and 114 long-term effect sizes and coded many other variables from 34 journal articles and research reports. The results show a mean short-term increase in use rates of 20.6 percentage points; the mean long-term effect was 13.7 percentage points. Large scale studies report smaller effect sizes than small scale studies; when studies were weighted by the (estimated) number of observations, the weighted mean effect sizes were 12.0 and 9.6 percentage points for the short and long term, respectively. The main factors that influence the magnitude of the reported short-term effect of the programs were the initial baseline rate (which was highly correlated with the presence or absence of a safety belt usage law), the type of population involved, whether incentives were delivered immediately or delayed, and whether incentives were based on group or individual behaviour. Together these four variables accounted for 64% of the variance. Other variables, such as the duration of the intervention, the probability of receiving a reward, and the value of the reward were not related to the short-term effect sizes. The relationship between moderating variables and long-term effects was less clear.@en