A.M. Salomons
Please Note
11 records found
1
Riding the circle
Cyclists' perceived safety and comfort in urban roundabouts
Perceived safety and comfort influence cycling mode choice and behaviour. While roundabouts are associated with a decreased severity of motor vehicle crashes, recent crash data in the Netherlands suggests that this is not the case for bicycle crashes, with 12% of all bicycle crashes between 2014 and 2021 occurring at roundabouts. Previous studies have mainly focused on intersection type and bicycle facilities, and overlooked how different design elements of dedicated bicycle facilities on roundabouts affect cyclists' perceived safety. Furthermore, previous studies did not investigate the relationship between perceived safety and comfort. To address these gaps, this study aims to better understand the factors contributing to cyclists' perceived safety and comfort at roundabouts. A total of 239 complete responses from cyclists to a stated preference survey were collected. A bivariate random effect ordered probit model was used to simultaneously model cyclist's perceived safety and comfort as a function of behavioural factors and infrastructural design elements. The results revealed that roundabouts where cars must yield to cyclists and with fewer vehicular entrance points were perceived by cyclists as safer and more comfortable. Also, cyclists' place of residence (in or outside the Netherlands), their likelihood to commit traffic violations, their recent crash history, and the type of bicycle they use, significantly affect their perceived safety. To improve cyclists' perceived safety and comfort in urban environments, it is recommended to ensure bicycle yielding priority, design dedicated bicycle facilities on roundabouts and maintain uniformity in bicycle infrastructure design.
Reference RL
Reinforcement learning with reference mechanism and its application in traffic signal control
This paper addresses the challenges of deploying reinforcement learning (RL) models for traffic signal control (TSC) in real-world environments. Real-world training can prevent mismatches between simulation environments and the actual traffic conditions, thereby achieving better performance of agent upon deployment. However, free explorations by agents during real-world training can disrupt traffic operations. To mitigate this, this paper proposes a reference mechanism to guide the decision-making process within the RL framework. A reference timing policy, typically a model-based signal strategy, is integrated into the learning process to provide agents with reference actions. Specifically, an additional Q-value function is introduced to evaluate both the agent's actions and those of the reference policy, allowing for adjustments before the actions are executed in real traffic system. Numerical results indicate that the reference mechanism effectively enhances system performance early in the training process, thus accelerating learning. We also combine the reference RL method with a pretraining procedure and a jump-start algorithm, respectively. Experimental results demonstrate their effectiveness in further enhancing system performance and facilitating real-world training.
Several factors were identified as influencing the emergence of these opinions, including influential articles published by news websites and time-dependent events such as the Covid pandemic. In response to these opinions, municipalities acknowledged citizen criticisms and implemented various strategies to address concerns. Personalized approaches, actively listening to individuals, and assuring them that their criticisms would be addressed, proved to be effective in engaging with the public. Explaining the control function for specific locations also resulted in positive reactions from 65% of respondents. However, when municipalities announced improvements to intersection layout or control without a personal touch, the reactions tended to be more negative: only 50% positive reactions for layout-related announcements and 40% positive reactions for control-related announcements. Negative reactions ranged from scepticism about effectiveness to questioning why similar solutions were not implemented elsewhere.
Concurrently, an empirical study was conducted to explore the relationship between different types of push buttons and cyclist behaviour at signalized intersections. The study made use of detector data and signal data provided by the municipality of The Hague, collected from eight intersections with 3-5 bicycle movements. Each signal for these movements was equipped with loop detectors and one of three types of push buttons: bright buttons, touch buttons, or touch buttons combined with a waiting time indicator, see Fig. 1. The bright button is a simple button which must be pushed and has small feedback lights (LEDs). The touch button indicates "wait BT11: Designing Infrastructure for Bicycle Traffic 178 for green" after being touched. The traffic controller of the intersection logs the data, so-called V-Log data, with which the following events can be determined per movement:
1. The start of red and green of the signals.
2. The moment the detector is occupied.
3. The moment the button was pushed or touched after the start of the red signal.
4. The number of times the button was pushed during red.
5. The duration of each button press/touch.
The buttons give feedback (the LEDs or the message “wait for green”) if pushed or touched, but also if a cyclist is detected by the loop detector, the button gives this feedback. The cyclist can still use the button after being detected by the loop detector, and the majority of cyclists will do so. For all movements at all intersections, and all buttons, on average in 0.7% of the cases only the button is used to request green, so without the loop detection. Averaged over the cycles, although the initial detection is done by the loop detector, the bright button is still pushed for 62% of the cases, The touch button is used in 85% of the cycles. It is unclear why this is higher since the touch button indicates the detection more clearly than the bright button. If the waiting time indicator is added, only in 44% of the cycles the button is still touched. There is only one movement with waiting time indicator, but it shows that when the remaining green time is clearly indicated, cyclists will use the button less.
There was no correlation found between the maximum waiting time and the number of button presses, suggesting that cyclists at these intersections do not express their impatience by pushing or touching the button more frequently. Further analysis showed that the touch button was touched for a longer duration compared to the bright button. In 85% of the cases, the bright button was pushed for less than 1 second, while only 45% touched the touch button for less than 1 second. Additionally, for the touch button (both with and without the waiting time indicator) a remarkable peak at 9 seconds was observed, with 8% of cyclists pressing the touch button for this extended duration. An explanation can be that the bright button gives clear tactile feedback when being pushed, which lacks for the touch button. In conclusion, despite the presence of negative opinions on push buttons, cyclists continue to use them. Negative opinions expressed on social media often relate to concerns regarding hygiene, waiting times, and safety issues. The recommended approach for municipalities is to implement personalized strategies and provide information to address these concerns. The empirical study found no significant correlation between using the button and waiting time. However, clear detection feedback, the tactile feedback of the ‘push’, and a waiting time indicator is advised for enhancing user comfort and trust.
...
Several factors were identified as influencing the emergence of these opinions, including influential articles published by news websites and time-dependent events such as the Covid pandemic. In response to these opinions, municipalities acknowledged citizen criticisms and implemented various strategies to address concerns. Personalized approaches, actively listening to individuals, and assuring them that their criticisms would be addressed, proved to be effective in engaging with the public. Explaining the control function for specific locations also resulted in positive reactions from 65% of respondents. However, when municipalities announced improvements to intersection layout or control without a personal touch, the reactions tended to be more negative: only 50% positive reactions for layout-related announcements and 40% positive reactions for control-related announcements. Negative reactions ranged from scepticism about effectiveness to questioning why similar solutions were not implemented elsewhere.
Concurrently, an empirical study was conducted to explore the relationship between different types of push buttons and cyclist behaviour at signalized intersections. The study made use of detector data and signal data provided by the municipality of The Hague, collected from eight intersections with 3-5 bicycle movements. Each signal for these movements was equipped with loop detectors and one of three types of push buttons: bright buttons, touch buttons, or touch buttons combined with a waiting time indicator, see Fig. 1. The bright button is a simple button which must be pushed and has small feedback lights (LEDs). The touch button indicates "wait BT11: Designing Infrastructure for Bicycle Traffic 178 for green" after being touched. The traffic controller of the intersection logs the data, so-called V-Log data, with which the following events can be determined per movement:
1. The start of red and green of the signals.
2. The moment the detector is occupied.
3. The moment the button was pushed or touched after the start of the red signal.
4. The number of times the button was pushed during red.
5. The duration of each button press/touch.
The buttons give feedback (the LEDs or the message “wait for green”) if pushed or touched, but also if a cyclist is detected by the loop detector, the button gives this feedback. The cyclist can still use the button after being detected by the loop detector, and the majority of cyclists will do so. For all movements at all intersections, and all buttons, on average in 0.7% of the cases only the button is used to request green, so without the loop detection. Averaged over the cycles, although the initial detection is done by the loop detector, the bright button is still pushed for 62% of the cases, The touch button is used in 85% of the cycles. It is unclear why this is higher since the touch button indicates the detection more clearly than the bright button. If the waiting time indicator is added, only in 44% of the cycles the button is still touched. There is only one movement with waiting time indicator, but it shows that when the remaining green time is clearly indicated, cyclists will use the button less.
There was no correlation found between the maximum waiting time and the number of button presses, suggesting that cyclists at these intersections do not express their impatience by pushing or touching the button more frequently. Further analysis showed that the touch button was touched for a longer duration compared to the bright button. In 85% of the cases, the bright button was pushed for less than 1 second, while only 45% touched the touch button for less than 1 second. Additionally, for the touch button (both with and without the waiting time indicator) a remarkable peak at 9 seconds was observed, with 8% of cyclists pressing the touch button for this extended duration. An explanation can be that the bright button gives clear tactile feedback when being pushed, which lacks for the touch button. In conclusion, despite the presence of negative opinions on push buttons, cyclists continue to use them. Negative opinions expressed on social media often relate to concerns regarding hygiene, waiting times, and safety issues. The recommended approach for municipalities is to implement personalized strategies and provide information to address these concerns. The empirical study found no significant correlation between using the button and waiting time. However, clear detection feedback, the tactile feedback of the ‘push’, and a waiting time indicator is advised for enhancing user comfort and trust.
When making trips in urban environments, cyclists lose time as they stop and idle at signalized intersections. The main objective of this study was to show how augmenting the situational awareness of traffic signal controllers, using observations from moving sensor platforms, can enable prioritization of cyclists and reduce lost time within the control cycle in an effective way. We investigated the potential of using observations from connected autonomous vehicles (CAVs) as a source of new information, using a revised vehicle-actuated controller. This controller exploits CAV-generated observations of cyclists to optimize the control for cyclists. The results from a simulation study indicated that with a low CAV penetration rate, prioritizing cyclists by tracking reduced cyclist delays and stops, even with a small field of view. As the delay of car directions were not taken into account in this study, the average car delay increased considerably with an increasing number of cyclists. Future work is needed to optimize the control that balances the delays and stops of cyclists and cars.
Bicycle network needs, solutions, and data collection systems
A theoretical framework and case studies
Intersection Control and MFD Shape
Vehicle-Actuated versus Back-Pressure Control
For perimeter flow control it is desirable that the MFD has a favourable and consistent shape, independent of fluctuations in traffic demand and of intersection signal variations. From literature it is known that a consistent shape is related to the homogeneity of vehicle accumulation in the sub-network. However, also the signal controller type may influence homogeneity and the MFD shape.
In this paper we investigate the relationship between the type of intersection control and the shape and scatter of the MFD, and the homogeneity of the sub-network, for Vehicle-Actuated (VA) and Back-Pressure (BP) control. The comparison of the two control methods is performed by means of microsimulation.
The results show that for both control methods the free-flow branch of the MFD has a low scatter with an average relative deviation around 2%. The congested branch shows a much larger deviation, 15% for the Vehicle-Actuated control, 16% for the Back-Pressure control. Furthermore, there is a distinct difference in the shape of the MFDs: for VA control the production increases faster as function of the accumulation than for BP control, but the network breakdown starts at a lower accumulation. So, based on the simulation results, VA control is better in undersaturated situations, and BP is better at higher accumulation levels. ...
For perimeter flow control it is desirable that the MFD has a favourable and consistent shape, independent of fluctuations in traffic demand and of intersection signal variations. From literature it is known that a consistent shape is related to the homogeneity of vehicle accumulation in the sub-network. However, also the signal controller type may influence homogeneity and the MFD shape.
In this paper we investigate the relationship between the type of intersection control and the shape and scatter of the MFD, and the homogeneity of the sub-network, for Vehicle-Actuated (VA) and Back-Pressure (BP) control. The comparison of the two control methods is performed by means of microsimulation.
The results show that for both control methods the free-flow branch of the MFD has a low scatter with an average relative deviation around 2%. The congested branch shows a much larger deviation, 15% for the Vehicle-Actuated control, 16% for the Back-Pressure control. Furthermore, there is a distinct difference in the shape of the MFDs: for VA control the production increases faster as function of the accumulation than for BP control, but the network breakdown starts at a lower accumulation. So, based on the simulation results, VA control is better in undersaturated situations, and BP is better at higher accumulation levels.