In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

Transportation is generally perceived as a catalyst for economic development. This has been highlighted in previous studies. However, less attention has been paid to examine the relationship between economy and transport demand by exploring spatially cross-sectional data, especially for countries with significant regional economic imbalance, like China. In this article, we assess the economic influence of intercity multimodal transport demand at the prefecture level in China. Spatial autoregressive regression models are used to examine the impact of transport demand on economy by deep analysis of transport modes (land, air, and water) and regions (eastern, central, and western). Through contrasting results from spatial lag model and spatial error model with those from the ordinary least square, this study finds that the estimation results can become more accurate by controlling for spatial autocorrelation, especially at the national level. Through rigorous analysis it is identified that except for water passenger traffic, all other intercity transport demand significantly contribute to a city’s economic development level in gross domestic product. In particular, air transport demands distribute more evenly and are estimated with the highest beta coefficients at both national and regional levels. In addition, the beta coefficients for land, air and water transportation are estimated with different magnitudes and significances at the national and regional levels. This study contributes to the ongoing discussion on the relationship between intercity multimodal transport demand and economic development level. Findings from this paper provide planning makers with valid and efficient strategies to better develop the economy by leveraging the special “⊣” cluster pattern of economic development and the benefits of air transportation.

In recent years, there has been increased interest in the use of bus IC card data to analyze bus transit time characteristics, and the prediction is no longer confined to rail traffic passenger flow prediction and traditional traffic flow prediction. Research on passenger flow forecast for the bus IC card has been increasing year by year. Based on the bus IC card data of Qingdao City, this paper first analyzes the characteristics of one-day passenger flow and passenger flow during subperiods and conducts a separate study on the characteristics of the elderly. The results show that the travel of the elderly is also affected by the weekday and the weekend. Then, based on the ARIMA model and the NARX neural network model, the passenger flow forecasting (10-minute interval) is carried out using the IC card data of No. 1 bus for 5 weekdays. The prediction results show that the NARX neural network model is effective in the short-term prediction of bus passenger flow, and especially, it is more accurate in the peak hour and large-scale data prediction.