# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 7 Conclusions  
This paper proposes a method for calculating and predicting the passenger pick-up probability on taxi-seeking roads. It uses the _BiLSTM_ neural network to predict the passenger volume for each road and calculates the pick-up probability for all roads through a passenger probability calculation method. The results show that the _BiLSTM_ deep learning model achieves a prediction accuracy of 9.67% higher than the BP deep learning model and 6.45% higher than the _LSTM_ deep learning model. Therefore, the BP network has a slower convergence rate, and its fitting performance is inferior to that of both the _BiLSTM_ and _LSTM_ networks. Under the same number of epochs, the _BiLSTM_ network outperforms the _LSTM_ network in fitting performance. This paper constructs a route recommendation model for taxi drivers seeking passengers and proposes a passenger-seeking route optimization strategy based on the NSGA-II algorithm, addressing the problem of recommending taxi-seeking routes under real-time changes in passenger pick-up probability. First, the taxi-seeking route recommendation model is built using concepts from graph theory and optimization theory. Then, the NSGA-II algorithm is designed to solve the route planning problem based on its specific characteristics. A gray comprehensive evaluation method is employed to assess the taxi-seeking route plans. Finally, using taxi trajectory data from Chengdu's main urban area during the evening rush hour, this study identifies taxi-seeking routes from Jinjiang Wanda to Chengdu North Station and from Chengdu North Station to the Fourth People's Hospital of Sichuan Province, along with their Manhattan distances and passenger pick-up probabilities. Based on the gray comprehensive evaluation method, factors such as travel time, number of bus stops, and driver ratings are used to evaluate the taxi-seeking plans. The results show that the proposed taxi-seeking route selection strategy increases the average passenger pick-up probability by 18.95% compared to common methods, effectively reducing the taxi vacancy rate. There are still some shortcomings in the methods proposed in this paper, which can be further explored in the future, specifically in the following two areas. First, there is a certain degree of subjectivity in the selection of factors influencing taxi passenger-seeking, mainly based on interviews with experienced taxi drivers and a review of the literature. This may lead to limitations and bias in the analysis. Therefore, future research needs to improve and refine a more comprehensive set of influencing factor indicators. Second, the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Options** & **Basis** & **Pathway Program** & **Passenger Probability** \\ \hline Option 1 & Shortest path & [1, 5, 6, 7, 8, 9, 13, 14, 15, 16, 21, 22] & 0.2473 \\ Option 2 & Highest passenger probability & [1, 2, 3, 11, 12, 13, 14, 17, 20, 22] & 0.4670 \\ Option 3 & Bus stops are the greatest & [1, 5, 6, 7, 8, 9, 13, 14, 17, 20, 22] & 0.4121 \\ Proposed & Synthesized assessment & [1, 2, 3, 11, 12, 13, 14, 17, 20, 22] & 0.4670 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of trail-finding alternatives (from 1 st recommended trail-finding point to 2 nd recommended trail-finding point). training of deep learning models requires continuous parameter optimization, but in this paper, the parameters were set based on experience without hyperparameter optimization. In the future, further optimization of the prediction model will be carried out. Conceptualization, G.G., B.L., D.Z., X.W., J.C. and T.W. ; methodology, G.G., B.L., D.Z., X.W., H.X. and Y.L. ; formal analysis, B.L., X.W., J.C., H.X. and Y.L. ; resources, G.G. ; supervision, G.G. and T.W. ; visualization, J.C. and H.X. ; project administration, G.G. ; writing--original draft, B.L., J.C. and H.X. ; writing--review and editing, [PERSON], X.W., T.W. and Y.L. All authors have read and agreed to the published version of the manuscript.