Changing behaviours through user interaction Presented by Urie Bezuidenhout Da Vinci Transport planning University of Auckland

So how bad is congestion? It depends on how you define congestion and what you compare it to $1billion per a?

What can we do about it? Medium Term Autonomous vehicles Medium to Long term Short Term Now

Influencing driver behaviour Short Term Now

Trajectory plots By Analyzing vehicle trajectories such as Last vehicle in platoon Time over distance plot of vehicle Makes it through green before red Successive vehicles then queue and take time to discharge the queue Some anticipate red onset and drive slower than normal Chrome text with reflection (Advanced) To reproduce the text effects on this slide, do the following: On the Home tab, in the Slides group, click Layout, and then click Blank. On the Insert tab, in the Text group, click Text Box, and then on the slide, drag to draw the text box. On the Home tab, in the Font group, click Character Spacing, and then click More Spacing. On the Home tab, in the Paragraph group, click Center to center the text in the text box. Enter text in the text box, select the text, and then on the Home tab, in the Font group, select Corbel from the Font list, select 60 from the Font Size list, and then click Bold. In the Format Text Effects dialog box, click Text Fill in the left pane, select Gradient fill in the right pane, and then do the following: Under Drawing Tools, on the Format tab, in the WordArt Styles group, click the arrow next to Text Fill, point to Gradient, and then click More Gradients. In the Font dialog box, on the Character Spacing tab, in the Spacing list, select Expanded. In the By box, enter 2. In the Angle box, enter 90°. Click the button next to Direction, and then click Linear Down (first row, second option from the left). In the Type list, select Linear. Also under Gradient stops, customize the gradient stops that you added as follows: Under Gradient stops, click Add gradient stops or Remove gradient stops until five stops appear in the slider Select the first stop in the slider, and then do the following: Select the next stop in the slider, and then do the following: Click the button next to Color, click More Colors, and then in the Colors dialog box, on the Custom tab, enter values for Red: 55, Green: 146, and Blue: 170. In the Position box, enter 0%. Click the button next to Color, click More Colors, and then in the Colors dialog box, on the Custom tab, enter values for Red: 223, Green: 240, Blue: 245. In the Position box, enter 50%. Click the button next to Color, click More Colors, and then in the Colors dialog box, on the Custom tab, enter values for Red: 67, Green: 74, Blue: 90. In the Position box, enter 56%. Click the button next to Color, click More Colors, and then in the Colors dialog box, on the Custom tab, enter values for Red: 185, Green: 171, Blue: 147. In the Position box, enter 60%. Select the last stop in the slider, and then do the following: Select the text on the slide, and then under Drawing Tools, on the Format tab, in the WordArt Styles group, click Text Effects, and then do the following: Click the button next to Color, and then under Theme Colors click White, Background 1 (first row, first option from the left). In the Position box, enter 90%. Point to Bevel, and then under Bevel, click Cool Slant (first row, fourth option from the left). Point to Reflection, and then under Reflection Variations, click Half Reflection, touching (first row, second option from the left). Under Drawing Tools, on the Format tab, in the bottom right corner of the WordArt Styles group, click the Format Text Effects dialog box launcher. In the Format Text Effects dialog box, click 3-D Format in the left pane, and then do the following in the right pane: Under Surface, click the button next to Material, and then under Standard, click Metal (fourth option from the left). In the Angle box, enter 80°. Under Bevel, next to Top, in the Width box, enter 4 pt, and in the Height box, enter 0.8 pt. To reproduce the background on this slide, do the following: Right-click the slide background area, and then click Format Background. In the Format Background dialog box, click Fill in the left pane, select Gradient fill in the right pane, and then do the following: Under Gradient stops, click Add gradient stop or Remove gradient stop until two stops appear in the slider. In the Position box, enter 72%. Click the button next to Color, and then under Theme Colors click Black, Text 1, Lighter 5% (sixth row, second option from the left). In the Position box, enter 100%. Click the button next to Color, and then under Theme Colors click White, Background 1, Darker 35% (fifth row, first option from the left).

Flushing out queues Queued @ Red Detector Zone Free flow Platoon   Detector Zone     Free flow Platoon A critical factor affecting the success of optimising a series of traffic signals is the accurate estimation and continuous adjustment of a suitable offset time lapse between the green phases of coordinated signals. However, effective offset times need to compensate for the presence of a downstream queue. Most traffic systems do not directly measure and only some estimate the magnitude of a queue. The presence of a queue is largely unknown when using the standard stop line placement of inductive loop detectors. SCATS instead uses derived traffic flow parameters and empirical relationships detecting reduced speed and reduced flow rate, and uses this as a proxy for indicating the presence of a queue. The lack of estimating the queue size and rate of growth can lead to saturation occurring faster than anticipated; especially when the queue growth rate exceeds the offset algorithm’s ability to adjust the offset times to allow discharge of the queue before the upstream platoon arrives. These arriving vehicles are then delayed by the back of the discharging queue, which in turn create shockwaves in the traffic stream, magnifying the congestion effect upstream of the queue. Queued Platoon

Research case study – Auckland CBD

Experimental evidence Sense traffic Optimise signals Drivers divert Route flow changes Signal timing changes Adaptive traffic control systems (ATCS) rely on the ability to detect changes in the system using sensors located at strategic locations, typically, single inductive loops at either at a location upstream of an intersection, or at the stopline. These point-collections of data are used to predict changes in the patterns of traffic flow in advance of the next cycle, with various optimisers adjusting the signal settings to anticipate the change in the flow. ATCS includes local controller logic by making phase switching decisions based on information from locally sensed vehicle demand. A critical component of the system is the density and sophistication of sensors to detect traffic flow conditions in the network. Signal coordination is typically achieved by applying higher-level constraints on the local controllers. At the heart of any adaptive system is a control algorithm that interprets available information and determines what the system should do. All adaptive systems contain a decision algorithm that either seeks a mathematical objective (e.g. minimise network delay) or, at a microscopic level, pursues an operational goal (e.g., avoid long queues).

Objectives Quantify theoretical maximum efficiency Signals can estimate queue Optimise OFFSET on cycle-by-cycle – queue minimisation Only selected corridors optimised Apply MUSIC - OPERA algorithm Drivers alter routes in response to signal changes The primary objective of this study is to quantify the maximum efficiency attainable over area-wide ATCS using downstream detectors under the following conditions: The ATCS can estimate the size of the queues downstream of an arriving platoon, The ATCS can optimise the offset for arriving traffic on a cycle-by-cycle basis, without restraint to other conditions, to minimise queueing delay. Only some of the vehicle corridors are optimised within an area, with the remainder left non-optimised that can be set to provide pedestrian priority instead. The optimisation of the selected urban arterial roads should not sacrifice the overall area-wide benefits. Drivers alter their routes in response to signal phase changes.   A case study located in Auckland, New Zealand, was used to quantify the magnitude of savings resulting from a combined split/offset optimisation routine with responsive driver behaviour along selected corridors that will achieve both a network-wide and corridor-specific efficiency. A limited set of arterials were optimised to cater for vehicle and public transport progression, with the remainder of corridors left unoptimised to provide pedestrian over vehicle priority.

Results – Network wide Short term (Medium term) Cyclical queues - 8% (-28%) Overcapacity queues - 4% (-31%) Stop-start queues - 39% (-59%) Travel time reduction - 5% (-20%)

OPERA OPTIMISING BENEFITS 11 min trip in peak hr 500,000 trips in peak hr -25 % travel time $ 400 Savings/person/year $0.25 billion /year Travel time savings for Auckland

Driver behaviour Competing signage Short block lengths Visually complex Queues Pedestrians and cyclists Appropriate sign placement is determined by the overall information presentation distance, which is the total distance at which the driver needs information about the choice point (e.g., intersection) as shown in Figure 1. This distance is the sum of the reading distance, the decision distance, and the manoeuvre distance. The reading distance is determined by the amount of time that the driver needs to read the sign’s message, depending on the number of words, numbers, and symbols contained in the message. The decision distance is determined by the amount of time needed to make a choice decision and initiate a manoeuvre. However, in the urban environment drivers typically have an information bottleneck at the approach to a signalised intersection. The message design is of vital importance given the unique site constraints.

Driver Workload + 6 s ~ 90 m In addition to a greater frequency of conflicts, intersections generally are more complex and difficult to navigate, compared to a motorway with its dual carriageway and well-spaced grade separated interchanges. In the urban environment the driver workload is often highest at the point when they need to comprehend unique and complex VMS messages compared to those messages found for instance, on a rural road. Figure 2 illustrates the workload drivers typically allocate to various phases of the approach to an intersection to help illustrate information bottlenecks.

Task analysis - intersections Ideally a VMS should be located in those zones that have the least opportunity for creating a bottleneck. Richard et al. (2006) analysis of information bottlenecks mentions that task pacing (self-paced or forced-paced) can have an effect on the difficulty of a particular subtask by affecting the time available to perform various tasks. Tasks can be perceptual (visually scanning roadway), cognitive (determine decelerating distance), or psychomotor (execute braking) and can be initiated either sequentially or simultaneously, depending on the demand of manoeuvring a vehicle. Individual tasks can be either self-paced, meaning that the driver generally has significant control over the timing and execution of task performance, or forced-paced, whereby performance involves task timing and execution that is mostly determined by factors outside of the operator’s control.

Fixation

Gaze Heatmap

Dynamic Hazards

Dynamic Hazards

calibrating drivers Use queues to discourage movements Flush out queues before arrival Greenwave along alternative route 4 - 6 weeks to get measurable benefits