Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework

Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is furthermore enhanced to account for traffic events that emerge in the simulation (e.g. passings by cars), which have an additional impact on cyclists’ decisions. Inspired by an evolutionary computing perspective, a randomizing router was implemented to enable cyclists to find realistic routes. It is discussed in detail why this approach is both feasible in practical terms and also conceptually consistent with MATSim’s co-evolutionary simulation approach. It is shown that meaningful simulation results are obtained for an illustrative scenario, which indicates that the developed methods will make real-world scenarios more realistic in terms of the representation of bicycle traffic. Based on the exclusive reliance on open data, the approach is spatially transferable

Design, characterization, and fabrication of solar-retroreflective cool-wall materials

Raising urban albedo increases the fraction of incident sunlight returned to outer space, cooling cities and their buildings. We evaluated the angular distribution of solar radiation incident on exterior walls in 17 U S. climates to develop performance parameters for solar-retroreflective walls, then applied first-principle physics and ray-tracing simulations to explore designs. Our analysis indicates that retroreflective walls must function at large incidence angles to reflect a substantial portion of summer sunlight, and that this will be difficult to attain with materials that rely on total internal reflection. Gonio-spectrophotometer measurements of the solar spectral bi-directional reflectivity of a bicycle reflector showed little to no retroreflection at large incidence angles. Visual comparisons of retroreflection to specular first-surface reflection for four different retroreflective safety films using violet and green lasers suggest their retroreflection to be no greater than 0.09 at incidence angles up to 45°, and no greater than 0.30 at incidence angles of up to 70°. Attempts to produce a two-surface retroreflector with orthogonal mirror grooves by cutting and polishing an aluminum block indicate that residual surface roughness impedes retroreflection. Ongoing efforts focus on forming orthogonal surfaces with aluminized Mylar film, a material with very high specular reflectance across the solar spectrum. We investigated (1) folding or stamping a free film; (2) adhering the film to a pre-shaped substrate; or (3) attaching the film to a flat ductile substrate, then shaping. The latter two methods were more successful but yielded imperfect right angles

Low-Stress Bicycling and Network Connectivity

For a bicycling network to attract the widest possible segment of the population, its most fundamental attribute should be low-stress connectivity, that is, providing routes between people’s origins and destinations that do not require cyclists to use links that exceed their tolerance for traffic stress, and that do not involve an undue level of detour. The objective of this study is to develop measures of low-stress connectivity that can be used to evaluate and guide bicycle network planning. We propose a set of criteria by which road segments can be classified into four levels of traffic stress (LTS). LTS 1 is suitable for children; LTS 2, based on Dutch bikeway design criteria, represents the traffic stress that most adults will tolerate; LTS 3 and 4 represent greater levels of stress. As a case study, every street in San Jose, California, was classified by LTS. Maps in which only bicycle-friendly links are displayed reveal a city divided into islands within which low-stress bicycling is possible, but separated from one another by barriers that can be crossed only by using high-stress links. Two points in the network are said to be connected at a given level of traffic stress if the subnetwork of links that do not exceed the specified level of stress connects them with a path whose length does not exceed a detour criterion (25% longer than the most direct path). For the network as a whole, we demonstrate two measures of connectivity that can be applied for a given level of traffic stress. One is “percent trips connected,” defined as the fraction of trips in the regional trip table that can be made without exceeding a specified level of stress and without excessive detour. This study used the home-to-work trip table, though in principle any trip table, including all trips, could be used. The second is “percent nodes connected,” a cruder measure that does not require a regional trip table, but measures the fraction of nodes in the street network (mostly street intersections) that are connected to each other. Because traffic analysis zones (TAZs) are too coarse a geographic unit for evaluating connectivity by bicycle, we also demonstrate a method of disaggregating the trip table from the TAZ level to census blocks. For any given TAZ, origins in the home-to-work trip table are allocated in proportion to population, while destinations are allocated based on land-use data. In the base case, the fraction of work trips up to six miles long that are connected at LTS 2 is 4.7%, providing a plausible explanation for the city’s low bicycling share. We show that this figure would almost triple if a proposed slate of improvements, totaling 32 miles in length but with strategically placed segments that provide low-stress connectivity across barriers, were implemented

Performance of Right-Turn Lane Designs at Intersections

Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes\u27 influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types

MeshAdv: Adversarial Meshes for Visual Recognition

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201