Hangzhou Binjiang District Big Data + Comprehensive Integrated Transportation Governance Project

A Demonstration Model for Comprehensive Traffic Governance in Megacities: Big Data Plus Holistic, Region-Wide Traffic Management

Case Study

Hangzhou High-tech Industrial Development Zone (Binjiang) serves as the hub and demonstration zone for Zhejiang Province’s digital industry strategy. It administers three subdistricts and 62 communities, with a permanent population of approximately 700,000 and a total area of about 73 square kilometers. The zone boasts a concentration of emerging industries, high land-development intensity, and a highly developed economy, ranking first in the province in per capita GDP. As the only administrative district in Hangzhou’s main urban area that does not impose vehicle restriction measures, Binjiang experiences significantly higher peak-hour motor-vehicle travel intensity than other districts. In 2019, the district’s peak-hour congestion index ranked second from last among Hangzhou’s main urban areas, making it one of the most severely congested road networks in the city.

Problem Analysis

The Binjiang District’s transportation system primarily faces four key issues:

(1) The road network structure is suboptimal, with traffic concentrated on the main arterial roads. Due to insufficient foresight in the early planning of Binjiang District regarding the pace of socio-economic development, congestion has become increasingly severe in recent years. In 2019, the peak-hour congestion index for Binjiang District’s road network reached 1.61, with frequent peaks exceeding 2.0 (indicating severe congestion). The district’s road network suffers from a “two shortages and one excess” problem: few collector roads, few inter-district connectors, and an excessive number of dead-end roads.

(2) Insufficient public transportation infrastructure results in a low modal share for commuting. As a hub for the digital industry, Binjiang features high land-development intensity, with industrial land-use ratios exceeding 3.0—more than twice the level in other central districts. However, the accompanying public-transit services are markedly inadequate, as evidenced by an insufficient number of bus routes and numerous service gaps, an uneven distribution characterized by dense service in the north and sparse service in the south, and inadequate feeder connections that fail to attract riders.

(3) Severe imbalance between workplaces and residences, leading to high demand for motorized travel. First, there is a pronounced imbalance between workplaces and residences, with clear pendulum-style commuting patterns. In Binjiang District, 73% of commuters travel across districts during peak hours, while only less than 30% commute within the district, indicating a severe misalignment in the spatial distribution of jobs and residences. Second, commuting distances are long, driving substantial demand for motorized travel.

(4) Insufficient refined management leads to low resource utilization. First, road resources are not utilized with sufficient granularity. In many roads in Binjiang District, traffic channelization is inadequate, resulting in low utilization of intersection resources and, consequently, severe congestion in specific directions at certain intersections during peak hours. Second, signal timing lacks sufficient refinement. Due to an underdeveloped perception infrastructure, signal timing is primarily based on single-point control and fixed cycles, with limited regional coordination and adaptive control, thereby keeping the overall level of intelligent traffic management relatively low. Third, parking resources are underutilized. Although Binjiang District has 222,000 parking spaces—representing a relatively high provision rate—the lack of sensing capabilities and shared-use applications for these spaces results in low efficiency in the mobilization and allocation of parking resources across the district.

Solution

(1) Governance Approach
Qianfang Technology has been deeply involved in the project’s construction as both the chief designer and the general implementer. Based on big-data analytics, Qianfang Technology has adopted a comprehensive, multi-pronged approach centered on “refinement plus intelligence,” focusing on optimizing road network structure, traffic organization, traffic engineering, public transportation, non-motorized transport, parking management, and technological control and supervision—approaches that have earned high recognition from the client.

In response to the specific traffic conditions in Binjiang, Qianfang Technology has innovatively developed a “Big Data + Comprehensive, City-Wide Traffic Governance” methodology tailored for key areas. By systematically addressing issues through steps such as “accurately identifying the root causes,” “implementing targeted interventions,” and “evaluating effectiveness,” the company has successfully carried out governance initiatives in demonstration zones including the Provincial Children’s Hospital, the Second Affiliated Hospital of Zhejiang University, the Tianjie Commercial District, and the Internet Industrial Park, thereby conducting meaningful exploratory practice in advancing the refined management of urban traffic.

(II) Governance Methods
At the macro level, leveraging big data as the foundation for comprehensive urban traffic governance, this approach emphasizes the enabling role of big data throughout the pre-, during-, and post-governance phases. It promotes refined traffic organization and intelligent traffic control systems, employing seven key integrated governance measures: optimization of the road network structure, optimization of traffic organization, optimization of traffic engineering, optimization of parking management, optimization of non-motorized transportation, optimization of the public transit system, and enhancement of technology-driven traffic management. Priority is given to addressing challenges in multiple settings, including schools, hospitals, commercial districts, industrial parks, and transportation hubs.

At the micro level, Qianfang Technology has proposed a targeted “1+X” governance framework, delivering a comprehensive package of measures to alleviate congestion. In this framework, the “1” represents the primary contradiction in any given scenario, while the “X” denotes a range of complementary strategies designed to address that core issue, thereby tailoring governance approaches to specific contexts such as schools, hospitals, and industrial parks.

Customer Value

1. In the first quarter of 2021, the peak-hour congestion index for the entire region decreased by 5% year on year.

2. The peak-hour delay index around the Binjiang Campus of Zhejiang University Children’s Hospital decreased by 25.8%, and following the implementation of traffic management measures, the average monthly patient volume at both Zhejiang University Children’s Hospital and the Binjiang Campus of the Second Affiliated Hospital of Zhejiang University increased by 120,000.

3. The average daily duration of congestion in the Longhu Tianjie commercial district decreased by 44.2%, and post-governance monthly foot traffic increased by 30,000 visitors.

4. Bus ridership around the Internet Industrial Park increased by 13.7%.

5. Fifty-seven “Seeking Knowledge” dedicated bus routes have been added around schools, providing daily service to more than 6,000 students and thereby indirectly supporting the “double reduction” policy in school education.