Large language models (LLMs) are revolutionizing user interactions in online map applications by enabling conversational interfaces with intelligent map agents. However, inherent ambiguities and nuances in human communication often lead to incomplete or unclear user instructions. While generating clarification questions can mitigate this issue, existing methods—which rely on prompt-based LLM assessments or supervised fine-tuning (SFT) of LLMs with limited annotated data—struggle to reliably determine when clarification is necessary, resulting in cold-start issues and reduced adaptability. To address this gap, we propose an uncertainty-aware dynamic planning framework for intent disambiguation in interactive agent systems, exemplified by Baidu Maps. Our framework leverages LLMs to dynamically generate agent action sequences while estimating query uncertainty. When uncertainty thresholds are exceeded, targeted clarification questions are triggered. Besides, to efficiently navigate the vast uncertainty space, we introduce a novel beam search-based pruning strategy. Crucially, the framework supports context-aware task planning without any additional model training. We evaluate our approach on Baidu Maps and a public robotic task, focusing on uncertainty management and adaptability. Experiments demonstrate that the framework achieves high precision across diverse LLMs without requiring an additional training phase, highlighting its versatility and robustness. Ablation studies confirm that the framework can significantly improve user intent recognition and task success rates.

Real-time bus travel time is crucial for the smart public transportation system and is beneficial for improving user satisfaction for online map services. However, it faces great challenges due to fine-grained spatial dependencies and dynamic temporal dependencies. To address the above problem, we propose GBTTE, a novel end-to-end graph attention network framework to estimate bus travel time. Specifically, we construct a novel graph structure of bus routes and use a graph attention network to capture the fine-grained spatial features of bus routes. Then, we fully exploit the joint spatial-temporal relations of bus stops through a spatial-temporal graph attention network and also capture the dynamic correlation between the route and the bus transportation network with a cross graph attention network. Finally, we integrate the route representation, the spatial-temporal representation and contextual information to estimate bus travel time. Extensive experiments carried out on two large-scale real-world datasets demonstrate the effectiveness of GBTTE. In addition, GBTTE has been deployed in production at Baidu Maps, handling tens of millions of requests every day.

Realtime bus waiting time information is of great importance to the intelligent public transportation system and is beneficial for improving user satisfaction by online map services. While there are limited realtime bus waiting time services in a city, because of the expensive cost of sensor deployment and sophisticated traffic conditions. To address the above problem, we propose Du-Bus, a multi-source data fusion based system, which estimates the realtime bus waiting time based on approximating the realtime locations of buses without GPS sensors, by a variety of urban datasets, including historical bus trip data reported by a limited number of GPS equipped buses, transportation network data, traffic condition data, user mobility data, and temporal data. Du-Bus approximates the realtime locations of buses without GPS sensors by jointly modeling the bus timetable and the bus realtime travel time, which can be estimated by a variety of data sources. Specifically, we first propose a BiLSTM based end-to-end model for each bus route to estimate the bus departure interval and generate the corresponding departure timetable. Then, we estimate the travel time for each individual bus via a deep neural network component by incorporating the traffic conditions, geolocation, and map query information. Finally, we estimate the bus waiting time for arbitrary stations in the city by jointly modeling the estimated bus departure timetable and travel time. We evaluate our system on two real-world datasets, and the results verify the effectiveness of Du-Bus compared with historical average based and headway based methods. Since early 2019, Du-Bus has been deployed on Baidu Maps, one of the world’s largest map services, servicing over 20 major cities in China.