Transit-oriented development (TOD) is among the most feasible strategies for relieving urban issues caused by the unbalanced development of transportation and land use. This study proposes a multiobjective TOD land use design framework for the optimization of the land use layout in station catchments. Given the high density and diverse development in Chinese megacities, a planning model that considers nonlinear impacts on ridership, land use efficiency, quality of life, and the environment is constructed. The model applies fine-grained geo-big data to fill gaps in the empirical and statistical data and improve practicability. A genetic multiobjective optimization approach without reliance on objective weighting is used to generate alternative land use schemes. A metro station in Shanghai is applied as a case study. The results indicate that the proposed ridership objective outperforms the commonly used linear function, and the optimization method has superior extreme optima and convergence to baseline models. We also discuss the consistencies and conflicts in the objectives and provide a balanced land use scheme considering local policies. This work provides suggestions for sustainable urban design with coordinated land use and transportation.

Lane detection is an important and fundamental task in autonomous driving. Modern convolutional neural network (CNN) methods have achieved high performance in lane detection; however, the intrinsic locality of convolution operations makes these methods limited in effectively modeling the long-range dependencies that are vital to capture global information of lanes. Additionally, numerous convolution operations result in considerable computational cost for high complexity. To overcome these difficulties, we propose an efficient lane detection method by combining CNN with a multilayer perceptron (MLP). First, an improved bottleneck-1D layer is used to replace the standard convolutional layer in overall network to reduce the computational cost and parameters while applying hybrid dilated convolution (HDC) to better capture multiscale lane information. Second, we construct a hybrid MLP block in the latent space to capture the long-range dependencies of lanes. The hybrid MLP projects tokenized convolutional features from spatial locations and channels, and then, they are fused together to obtain global representation, in which each output pixel is related to each input pixel. The introduction of MLP further decreases computational complexity and makes the proposed architecture more efficient for lane detection. Experimental results on two challenging datasets (CULane, Tusimple) demonstrate that our method can achieve a higher computational efficiency while maintaining a decent detection performance compared with other state-of-the-art methods. Furthermore, this study indicates that integrating the global representation capacity of an MLP with local prior information of convolution is an effective and potential perspective in lane detection.