We present experimental and theoretical evidence on the breakdown of the simple man's model in the high-energy recollision region by examining both the energy-resolved angular distribution of photoelectrons in above-threshold ionization and the spectrum of high-order harmonics generated in elliptically polarized driver fields. With the help of a semiclassical model calculation, we show that, surprisingly, even for the high-energy recollision electrons, their wave-packet dynamics can be influenced strongly by the Coulomb field.

This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization information, including vehicle speed, axle spacing, detection of both vehicle axles and moving direction, can also be extracted from the collected seismic signals as demonstrated in this paper. The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle. Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

The paper aims to assess bus accessibility considering the matching between supply and demand for effectively optimizing the level and fairness of urban public transport service, which realizes the quantification of regional balance and accurate positioning the area with the worst balance. We firstly employ hotspot detection procedure based on taxi trajectory data and kernel density analysis to identify the travel sensitive areas, the heat values of which are deployed to represent travel demand spatial-temporally and evaluate weight factors for bus accessibility modeling. Matter-element theory is selected to establish multi-parameter evaluation model of bus accessibility, which has potential for solving incompatibility problems by systematically considering all factors. The correlations between accessibility indexes and heat value are deployed to evaluate weight factors rather than analytic hierarchy process or expert assessment method to improve subjectivity and dynamic updating. An index called the Level Ratio of Accessibility to Demand (LRAD) is addressed finally to quantify the balance between accessibility supply and travel demand of travel sensitive areas, which identifies the regional imbalance to assist the public transport system assignment. Xi’an, a large city, is selected as a case study for methodology verification. Bus accessibility degree of the whole city as well as its travel sensitive areas is evaluated by the matter-element model. It is found the bus transport accessibility of Xi’an is moderate level(M3). The LRAD results identify the priority-processing area with the poor balance between accessibility supply and travel demand, which is cross referenced with local urban plans for verification.

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along the lane separation lines instead of the intrusive middle of the lane detection sensors. The key innovation in SRS design is its ability to detect vehicles in two adjacent lanes in real time by employing a three-axis geomagnetic sensor mounting on the lane line, which is the basis of advanced applications such as traffic surveillance, driving guidance, and congestion monitoring. Multiple vehicles are recognized based on the superposition of the disturbance of the geomagnetic fields. Magnetic field changes along the axes are simulated and analyzed with vehicles modeled as magnet dipoles to determine the criteria for the detection of vehicles in adjacent lanes, which are verified by actual waveforms. Extended dual-window and two-lane vehicle detection algorithms are developed, enabling to distinguish five detection scenarios and count vehicles in two lanes. Field test results show that one SRS can detect traffic in adjacent lanes with a correct vehicle detection rate of over 98%, the same level achievable by intrusive middle of the lane detectors. Based on the enhanced features, high performance, cost-effectiveness, and value-added services, the SRS is a significant improvement on the conventional studs for intelligent transport system applications.

Intelligent transportation infrastructure has gained significant research attention recently. In this paper, an innovative sensor network of smart road stud (SRS) is developed to enhance traffic detection infrastructure characterized by its functionality in traffic data collection, long/short range wireless data transmission, self-sustained power supply, and remote custom controlled lighting-based traffic guidance. Compared to the traditional traffic detectors and road studs, SRS nodes are installed on lane lines instead of lane center to enable the additional applications besides the detection function, such as traffic monitoring, congestion warning, routing guidance, and so on. SRS detects vehicles based on three-axis geomagnetic sensors. A vehicle detection algorithm is proposed correspondingly under different operation scenarios to count vehicles in two adjacent lanes. Its detecting accuracy can be further improved by a sensor network of multiple SRSs working cooperatively. Field test results show that the vehicle detection accuracy based on the SRS network is about 98% per lane, which is the same level as the commercial detector installed in center of lane, even under the non-standard driving behaviors such as crossing lane line. The high performance, value-added service, and low cost enable wide-range applications of SRS networks as part of intelligent traffic detection infrastructure.

A self-designed AMR detector named Smart Road Stud (SRS) is proposed. SRS, installed along lane markings, is not only a detector but also a lane markings enhancement device. Because a single SRS needs to detect vehicles on two lanes, the vehicle detection method for SRS is different from the AMR detectors installed in the middle of the lane or at the roadside. Based on SRS, an innovative mathematical model based on the magnetic dipole is developed to simulate vehicle magnetic field. According to the model, lane information can be inferred to achieve traffic volume detection. Results show that a single SRS delivers traffic volume detection accuracy of 97%, providing a strong foundation for future research to achieve higher detection performance based on multi-SRS linkage. The significance of this paper is to meet the detection requirements of warning equipment installed on lane markings.