This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.

This paper presents CurveLight, an accurate and practical light positioning system. In CurveLight, the signal transmitter includes an infrared LED, covered by a hemispherical and rotatable shade, and the receiver detects the light signals with a photosensitive diode. When the shade is rotating, the transmitter generates a unique sequence of light signals for each point in the covered space. The main novelty of the system design is a set of curves that define different regions, either transparent or translucent, on the shade. The regions allow the light signals to create patterns from which the receiver can calculate its angles with respect to the transmitter. We design the curves in such a way that the angular information is most robust to errors caused by signal noise and motor jitters. Moreover, the shade is divided into multiple sectors, each providing independent positioning function, so as to maximize the position update rate. Experiments in various environments show that the system achieves 2-3 cm accuracy on average, with a 36 Hz update rate with a single transmitter. We present a product quality implementation of the system, and report the deployment experience in real-world environments, including autonomous driving and robotics navigation. CurveLight consistently offers centimeter-level accuracy and low latency, serving as a key component of the hybrid navigation solution for real systems in challenging scenarios.

The proper location of weigh-in-motion (WIM) stations in road networks is critical to effectively reduce the impact of overweight trucks. Truckers may quickly learn the locations of WIM stations, and take detours to bypass these checkpoints, with the aid of advanced navigation systems. This reaction needs to be considered in the location models for the deployment of WIM systems. This paper proposes a bilevel model to deal with the WIM location problem in a road network. The model includes an upper level and multiple lower level models respectively, represent the decision-making behavior of the law enforcement officials and truckers for different origin-destination pairs. The upper level model determines the optimal number and locations of WIM stations so as to minimize the damage due to evasive overweight trucks, considering the truckers' route choices. The lower level models simulate the truckers' route choices in response to the WIM locations determined by the upper level model. A heuristic is developed to efficiently solve the bilevel model, as it is difficult to obtain the exact solution. The proposed model and heuristic are evaluated using a test instance generated based on the Nevada road network. The results show that the heuristic outperforms a classical approach based on k shortest paths.

Carpooling is one method that can be easily instituted and can help resolve a variety of problems that continue to plague urban areas, ranging from energy demands and traffic congestion to environmental pollution. However, most carpooling organizations currently use a trial-and-error process, in accordance with the projected vehicle travel times, for carpooling, which is neither effective nor efficient. In other words, stochastic disturbances arising from variations in vehicle travel times in actual operations are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule could be disturbed enough to lose its optimality. Therefore, we constructed a stochastic carpooling model that considers the influence of stochastic travel times. The model is formulated as an integer multiple commodity network flow problem. Since real problem sizes can be large, it could be difficult to find optimal solutions within a reasonable period of time. Therefore, we develop a solution algorithm to solve the model. To test how well the model and the solution algorithm can be applied to the real world, we also developed a simulation-based evaluation method. To test the model and the solution algorithm, a case study is performed based upon data reported from a past study carried out in northern Taiwan. The results show that the model and solution algorithm are good and could be useful for carpooling practices.

In this paper, we develop a model for emergency repair problems under large-scale supply-demand perturbations. The model formulation proposed in this paper has the following key features. First, a novel time-space network flow technique is adopted to generate detailed schedules for repair teams and allow dynamic updates of the network due to perturbations. Second, the original schedules prior to the perturbations are considered by controlling the total difference between the original schedule and the adjusted schedule. Third, to reduce computational complexity, the model is formulated with different levels of detail (individual teams versus a team group). The model is also formulated as a special mixed-integer network flow problem with side constraints, which is characterized as NP-hard. An ant-colony-system-based hybrid global search algorithm is developed to efficiently solve large-scale problems. To test how well the model formulation and the heuristic algorithm may perform in actual operations, we conduct a case study using actual data from the 1999 Chi-Chi earthquake in Taiwan. The results show that the proposed model and solution algorithm perform very well and thus have great potential for assisting with the making of emergency repair decisions in the event of disasters given large-scale perturbations in supply and demand.

Long-term car pooling is defined as the sharing of a private vehicle by more than one user who need to reach a destination following a semicommon route between the individuals' points of origin and destination in a specific period. In this paper, we employ a network flow technique to systematically develop a long-term many-to-many car pooling model. The model is formulated as a special integer multiple-commodity network flow problem. A Lagrangian relaxation-based algorithm is also developed to solve the model. The performance of the heuristic algorithm is evaluated by carrying out a case study using real data and suitable assumptions. The test results confirm the usefulness of the model and the heuristic algorithm and that they could be useful in practice.

In this paper, we develop an intercity bus scheduling model for a variable market share. The proposed model, unlike the past intercity bus scheduling models, does not assume a fixed market share. Instead, passenger choice behaviors in competitive markets are considered. The model is formulated as a nonlinear mixed integer program that is characterized as nondeterministic polynomial-time hard. We develop a solution algorithm to efficiently solve the model. The test results, which are based on a major Taiwan intercity bus carrier, show the good performance of the model and the solution algorithm.